Sage Intacct, Oracle ERP Cloud, and Microsoft Dynamics 365 ERP are the three highest-rated ERP systems by their users.
86% of Unit4 ERP users say their CRM system is the best of all vendors in the study. The survey-wide satisfaction rating for CRM is 73%, accentuating Unit4 ERP’s leadership in this area.
85% of Ramco ERP Suite users say their ERP systems’ analytics and reporting is the best of all 22 vendors evaluated.
These and many other insights are from SoftwareReview’s latest customer rankings published recently in their Enterprise Data Quadrant Report, Enterprise Resource Planning, April 2021. The report is based entirely on attitudinal data captured from verified owners of the ERP systems reviewed. 1,179 customer reviews were completed, evaluating 22 vendors. SoftwareReviews is a division of the world-class IT research and consulting firm Info-Tech Research Group. Their business model is based on providing research to enterprise buyers on subscription, alleviating the need to be dependent on vendor revenue, which helps them stay impartial in their many customer satisfaction studies. Key insights from the study include the following:
Sage Intacct, Oracle ERP Cloud, Microsoft Dynamics 365 ERP, Acumatica Cloud ERP, Unit4 ERP and FinancialForce ERP are most popular with their users. SoftwareReview found that these six ERP systems have the highest Net Emotional Footprint scores across all ERP vendors included in the study. The Net Emotional Footprint measures high-level user sentiment. It aggregates emotional response ratings across 25 questions, creating an indicator of overall user feeling toward the vendor and product. The following quadrant charts the results of the survey:
80% of Acumatica Cloud ERP users say their system helps create more business value, leading all vendors on this attribute. How effective an ERP system is at adapting to support new business and revenue models while providing greater cost visibility is the essence of how they deliver business value. The category average for this attribute is 75%. Of the 22 vendors profiled, 12 have scores at the average level or above, indicating many ERP vendors are focusing on these areas to improve the business case of adopting their systems.
86% of Sage Intacct ERP users say their system excels at ease of implementation, leading all vendors in the comparison by a wide margin. Implementing a new ERP system can be a costly and time-consuming process as it involves extensive training, change management, and integration. Ease of Implementation received a category score of 75% across the 22 vendors, indicating ERP vendors are doubling down investments to improve this area. Just 11 of the 22 ERP vendors scored above the category average.
Spotify is very much an engineering led company. It is also a product led company. Therefore when it decided it needed to get a better handle on its cloud spend, it decided to build an internal product designed to be used by its engineers. The result is Cost Insights, a plugin for Spotify’s Backstage developer portal. The idea is that engineers and engineering teams are incentivised to take more responsibility for the costs associated with the products they’re building. Modelling cost becomes part of the engineering process, rather than being a separate process for finance teams to manage.
Spotify cut its annual cloud spend by millions of dollars by helping engineers to make better decisions about resource allocation. Why was this so important? The answer is pretty simple. Infrastructure costs were outpacing user acquisition so Spotify set out to bend that curve to better align engineering and business goals.
When forecasted cloud costs are growing at a rate faster than income and revenue, it’s time to make changes. Naturally, Spotify thought it through as an engineering problem.
Spotify has been successful with its approach and is now ready to help other companies that are having the same problem. Spotify therefore open sourced Cost Insights in October 2020 as part of its broader open source and community efforts with Backstage.
Spotify certainly isn’t alone. As organisations migrate more and more workloads to the cloud, and build new applications and services there, they’re finding that cost sprawl is very real. Service ownership is a problem – who is responsible for the bill, and keeping costs down?
I spoke to the Spotify Cost Insights team in some depth to learn more. In this post we will look at Spotify’s culture, approach, technology and what it hopes to achieve with Cost Insights, but also how other companies can begin to adopt some of the same approaches based on Spotify’s practices. This research is based on interviews with Spotify employees, which are found in the companion videos, embedded below. These include a RedMonk Conversation about the issues raised, but also a video in our What Is/How To format, which includes a demo (see below for that sweet, sweet video content.)
Spotify Culture
At Spotify, speed is a core cultural principle.
CEO and founder Daniel Ek sums it up:
We aim to make mistakes faster than anyone else.
With cloud computing though, focusing on speed means costs can spiral out of control. The Spotify cost optimisation team has spent a lot of time ensuring they’re building tools that are relevant to both new product teams, and mature businesses we have at Spotify, and reconcile that with sustainable cloud costs.
Spotify has almost five hundred engineering teams today. It does about twenty thousand deployments a day across thousands of micro services. That’s a huge amount of complexity to manage, and it’s not really amenable to simply pointing at spreadsheets and saying: “we need to fix this”. Instead Spotify hacked the culture. It went back to basics and thought about cost as a problem to be solved with its engineering culture.
Spotify engineering teams are used to having a lot of autonomy, so the company couldn’t just introduce new cost guardrails as a top down concern. Autonomy is a core value at the firm, so Cost Insights was built with that in mind. Therefore the Cost team tried to foster a culture where optimisation would be fun.
More on Backstage
Cost Insights already had a natural home.
Backstage is Spotify’s developer portal. The company’s entire software delivery supply chain is managed with Backstage – all components, data, pipelines, and services are managed using the platform, from idea to production, including monitoring and observability. The core idea is to provide a single, consistent UI for all infrastructure tooling, services, and documentation. Backstage is the company’s front end for all engineering tasks. Both developer and operational workflows are integrated into one console. Backstage is built around a service catalogue enabling teams to track the infrastructure underpinning the services and products they’re building.
Core Backstage use cases include:
Creating a new microservice
Following a pull request from review to production
Centralized technical documentation
Review performance of a team’s mobile features
Backstage abstracts a lot of complexity, whether starting a Kubernetes cluster, or provisioning a pipeline. For example – Spotify uses Jenkins under the hood for CI, but engineers don’t need to know that: they just use GitHub Enterprise and Backstage.
Backstage is not just a developer portal – a better description might be a platform for building developer portals and managing software delivery. Spotify is open sourcing these components of its own Developer Experience (DX) infrastructure, for use by the engineering community. Given that many organizations see Spotify culture and processes as aspirational – the much vaunted “Spotify Model” – it makes sense that folks would adopt their tools. We’ve seen this Netflix, and open source tools such as Chaos Monkey, Spinnaker and Zuul.
Backstage was adopted as a Sandbox Project by the Cloud Native Computing Foundation in September 2020, and there has been a steady uptick in contributors and folks building their own plugins. Spotify is being intentional about building third party interest: more than 40% of pull requests are now from external, non-Spotify, contributors.
Backstage also brings an opinion about design to the table – what plugins should look like, and how they should be built, which should be helpful as Spotify grows the community. Great user experiences can definitely be supported with effective design systems.
We believe an elegant, cohesive UX is vital to what makes Backstage such a productive, end-to-end development environment.
The Stack
Backstage is a modern stack, written primarily in Typescript with a React front end. The front end code is written with React, which defines the platform’s plugin architecture. It uses the Yarn package manager and the Lerna monorepo library. Code formatting is with Prettier, with ESLint for linting. Being a modern stack the API Gateway is of course GraphQL-based.
In terms of deployment, Backstage does a solid job of supporting Kubernetes environments, notably GKE, given that’s what Spotify uses inhouse. For documentation Spotify has embraced a docs-as-code approach with MKDocs.
Cost Insights and Labels
The fact that Backstage is already central to how engineers and engineering teams work at Spotify made it the natural home for Cost Insights. Spotify wanted cost optimisation in a place where engineers are already spending the bulk of their time, as opposed to trying to encourage them to use a different third party system.
Backstage was also a place to bootstrap Cost Insights because it is based on a service catalogue model, with the graph of services already in place. Third party organisations wanting to take advantage of this functionality are going to have to do some upfront work modelling and mapping their internal APIs and services to their own products and services, but building this kind of service map is a useful exercise for any organisation.
Cost Insights also supports labels. Engineers can label Cloud provider resources so that they match their own internal terms, components and service names, rather than relying on billing info from the Cloud provider itself.
One useful aspect here is that often in an organisation a shared infrastructure service used by multiple teams is not properly represented in terms of billing. One team ends up paying for that service, rather than it being properly allocated. Backstage modelling helps with internal chargeback.
MOAR FTEs, please
Spotify largely views cost through the lens of engineering resources. If you invest X to automate a process or optimise a system, what are the returns in terms of Full time Employees (FTEs) – at Spotify that basically means Engineers – you could potentially hire with the savings.
Early experiences with Cost Insights allowed Spotify to fund the equivalent of 25 teams across the company. One interesting part of the Spotify cost optimisation journey is that it is not based on driving specific team OKRs. Engineers and teams don’t get the money back that they save, to spend on their own product, but rather it goes back into the company. The incentives are communal and cultural rather than directly financial. Cost Insights could be used as part of engineering bonus structures, at more financially oriented companies. But for Spotify at least the idea is simply to optimise so that everyone in the company benefits. This communitarianism probably reflects the fact Spotify is a Swedish company, with Scandinavian values.
Developers generally like specificity however, regardless of where they’re from. An engineer has trade-offs to make. If a particular service is becoming too expensive, for example, is there an alternative approach from the Cloud provider that might save money in the long run?
Is this migration worthwhile? How much will it save? How much engineering effort should they put in it? Spotify wants an engineer to be easily able to make a calculation based on how many engineers they need to use on a migration or automation in order to save further engineering resources.
If it’s going to take months of effort and only save half an engineer headcount then an “optimisation” is probably not worthwhile. It’s not enough to simply present cost information. It needs to be granular and mapped to services. Engineers want to know what’s the problem and where is it coming from? That way they can make better decisions.
Natural Optimisers
One facet of Cost Insights that really strikes a chord is the focus on encouraging engineers to do more of what they already like to do.
According to Janisa Anandamohan, Spotify Senior Product Manager, Cost Engineering:
We know engineers are natural optimisers when it comes to reliability, security, performance, et cetera. And now we’re telling them, hey, add costs into the mix. And they were super excited about that. We had many teams that were just able to tweak their services and data pipelines and to make them more efficient.
And we know efficiency doesn’t just help cost, but helps reliability and performance as well. So we were getting double, triple wins along the way.
Spotify further encouraged its developers in a couple of ways. One is to provide recommendations within the portal – for example, with advice about auto-scaling strategies.
The second is with an internally crowdsourced document called Our Cookbook, for engineers to share what worked for them in terms of system optimisations, to help other teams.
Spotify realised that developers were treating cost optimisation like a game, showing off their wins, and encouraging other teams to do the same thing. This helped create a virtuous circle, to “bend the curve”. Leader board functionality is therefore on the roadmap, to help the social, competitive aspects of cost control, as teams compete to reduce costs across services, and better learn from their peers.
Help from the team
While Spotify expects engineers to engage and take some responsibility for cost management, sometimes they need a nudge, and different teams take to it differently. At Spotify cost management is a conversation between engineers and the business, which is represented by a specialist cost management function.
Some teams got access to Cost Insights and immediately started using it to figure optimisations and quickly put them into place. Other teams, the Spotify Cost management team works with and asks why a service is costing so much. Often the team has a good reason for cost expansion though. For example a new service is likely to incur extra cost while it’s in startup mode. A more mature service though should see margins improving.
At times when the cost management organization feels it needs to intervene, they take a collaborative approach to optimisation, for example helping the team in question to figure out how to optimise a workflow, for example, that’s running data processing expensively. It’s a consultancy model.
Spotify argues that having these conversations, bridging bottom up and top down, helps improve alignment in the business. Cost Insights takes finger-pointing out of the equation.
Cost Insights as a product
Spotify hasn’t just built some code for itself and licensed it as open source, then hope it sticks. It is explicitly managing the Backstage development process in terms of product management, and has a full team working on it. The business model isn’t clear yet, but Spotify clearly believes it needs to build a business model for the product in order to encourage sustainable adoption. The product has its own logo.
In the case of Backstage plans arguably go further, than for example, Netflix with Chaos Monkey. Spotify wants to take more responsibility for third party use of the software. Spinnaker might be an interesting parallel, which while it began as a project at Netflix, now has a startup, Armory, building products and services around it. Or Kafka, which began as a project at Netflix, and now has companies built on the technology, notably Confluent. Commercial Open Source is a business in its own right, and Spotify is not in that business, so it will be interesting to see how this plays out. One startup has already emerged with a hosted support model for Backstage, with a cool name. Who doesn’t want to be a Roadie and have a backstage pass at a great gig?
For example, while Cost Insights–like Backstage–is intended for use by other companies, it was initially designed for use at Spotify. This creates some interesting product management challenges. Notably, Cost Insights is primarily designed for Google Cloud, given Spotify primarily runs on GCP, but AWS support is planned. Who will pay for functionality that isn’t core to Spotify’s needs? That’s where a sustainable open source model comes in.
What is FinOps?
Cost Insights is not for the average enterprise. It’s explicitly positioned for use by high scale companies like Spotify – with its 2k+ microservices and 4k data pipelines, to help numbers be attributed to specific teams, products and services.
Spotify also shares notes on a regular basis with peer cloud companies, the biggest customers of hyperscale cloud providers, about best practices in high scale operations, technology and spend. It also uses the term FinOps, shorthand for Cloud Financial Management. The FinOps Foundation is run by the Linux Foundation.
The FinOps argument is interesting, because there is quite a difference between the needs of Spotify and its peers and those of more traditional enterprises. Even the most digital enterprise is not using anywhere near the kind of capacity of a Netflix or Uber.
There are a host of FinOps vendors out there, providing Cloud Financial Management. Cloudability, CloudHealth and ParkMyCloud, are well known vendors. And of course you have AWS Cost Explorer, Amazon’s own tool.
AWS revenues in Q4 2020 were $12.7 billion. Microsoft’s Cloud revenues were $43.1 billion for the second quarter of fiscal 2021. Google Cloud’s revenues for 2020 were 13.06 billion.
Cloud is taking an increasing share of customer IT budgets in almost all product categories That’s a lot of cost to optimise, particularly given the huge number of AWS services that customers are using, and are constantly being introduced. FinOps vendors generally come at the world from the perspective of the finance person rather than the engineer, though. As Spotify looked to reduce its spend, its first initiative–before engineering-centred initiatives like Cost Insights–was top down, focusing on better forecasting and pre-booking GCP resources.
Not everyone is a fan of the “FinOps” movement. Corey Quinn, co-founder of Duckbill Group, is almost certainly the best cost optimisation expert on the planet when it comes to high scale use of AWS services. If you’re spending tens of millions of dollars a year on AWS you should contact Corey to reduce your bill before you finish reading this post. Duckbill offers fixed fee spend management advice with a solid engineering understanding of AWS services and how to get the best out of them. Corey believes that FinOps needlessly complicates things, and that good practices by developers – for example turn things off when you’re not using them! – is the best way forward.
You should be enabling engineers to better understand their total cost of service, which brings us back to tools like Cost Insights.
Label Up to Optimise
We discussed labels above. One of Spotify’s earliest efforts in the space was to apply labels to every cloud resource that it could in Spotify terms and language, rather than that of the cloud provider. This means that when an engineer looks at this tool, they can immediately see things that they understand in terms of their day to day work, rather than trying to work out what it means in GCP terms.
These data are quite granular – showing license by cloud product, so for Google Data Flow or data processing, the developer sees costs broken down by pipeline, or say for Google Compute Engine, the dev sees Spotify service names. That information can then be correlated and configured with different business metrics – e.g., daily active users, or monthly subscribers. The platform is extensible because Spotify of course expects third party users to have their own KPI and metrics.
Sustainability Goals
Cost isn’t the only factor that can be optimised. One interesting idea is that Cost Insights can also be used to optimise this sustainability of a service, deciding in what region, how and when a service runs, and ensuring that it takes advantage of a data centre running on renewable energy.
Spotify includes language in Backstage recommendations about optimisation for sustainability. The team built a feature where the user can switch between its normal FTE (engineer) conversion, to carbon offset tonnes.
RedMonk would like to see this sustainability view adopted by user organisations because it might help large cloud buyers to put more pressure on AWS to focus on renewable energy. AWS is on the record as saying more customer pressure will direct the firm to accelerate its sustainability efforts.
Cadence and Anomalies
One question about shifting cost optimisation left is – what should the cadence look like? It’s not like unit testing, for example, which happens as a constant process of improvement.
Spotify found that the best way to get involvement was to encourage engineers to use Cost Insights in a cadence alongside existing quarterly planning. If there were issues that the Cost team felt needed attention, they’d alert a team before those meetings. That said, if costs are rapidly escalating out of control for a particular service, that’s something that should generate an alert, so anomaly detection is on the Cost Insights product roadmap.
All that said, Cost Insights does have a prime place in Backstage, so the information is always there for developers to consider as they make trade-offs and decisions about infrastructure choices.
Concluding
The move to create a rich ecosystem around Backstage is going to be an interesting new challenge for Spotify, involving a lot of work and new trade offs around what are, or are not, core assets. Building for others may for example slow down development for functions that Spotify needs, which when velocity is a core company value will be an interesting challenge.
But Spotify is right that cloud costs are growing out of control at many companies as they increase their investments in the cloud. That being the case, they’ll be looking for tools that improve their effectiveness in that area. The market for Cost Insights won’t necessarily be huge, but it could be a very influential slice of the industry cloud spend.
RedMonk has done some work with Spotify, but this post was not sponsored, and is an independent piece of research. AWS, Google Cloud and Microsoft are all RedMonk clients.
In scaling the cash-flow side of SaaS, there’s almost nothing more powerful than annual contracts combined with prepaid cash. It’s just such a huge benefit. You get all the cash up-front, and your churn almost by definition goes down. Because the earliest chance the customer has to churn is 12 months hence.
So get pre-paid annual deals whenever possible
At Adobe Sign / EchoSign, half the reason we went cash-flow positive at about $5m in ARR was prepaid annual and multiyear contracts. Combine that with 100% or greater annual growth, and once you hit a few million in ARR, prepaid annual contracts bring in a lot of extra cash ahead of time. Cash you can use especially to make those accretive hires (more on that here).
Our KPI here in fact was to bring in at least 120% of our monthly MRR in cash, each month. We usually hit it. More on how here.
But … to go to annual pricing or not …
It’s a bit of a false choice for most vendors. Because the world already works certain ways here based on the context:
Bigger companies want to sign annual contracts, especially in exchange for discounts. At least, once you have enough of a mini-brand to be trusted. It’s how big company procurement and budgeting processes work. Nothing is a bigger headache in a Fortune 500 company that having to go back to procurement every single month to get an invoice approved.
and … Most small businesses and individuals generally want to pay monthly on their credit cards. Even if you provide an annual discount. Not all – but most.Think about yourself as a consumer. Very small businesses often buy more like consumers than large enterprises. How often do you yourself prepay annually? Probably only for a subset of services you are confident you want for the long term.
So there is a natural continuum of how customers like to pay, and how they are comfortable paying.
A great example is Zoom. As Zoom use exploded during Covid, even more SMBs needed Zoom. And as a result, even more chose monthly subscriptions. Zoom monthly billing exploded to 50% of ARR as SMB use grew 5x during Covid! More on that here.
If you offer bigger companies a natural incentive to sign up annually, they will. At least, once you have a brand and they know they can trust you on Day 0. And later, you can even force them to and make it the only option. But if you ask tiny businesses and individuals to pay for a whole year upfront, they will hesitate. At least the vast majority. So offer them a discount to do so (often 20%, a la the WP Engine above). And some, but not most, will.
Figure out who you sell to, and why, and offer the options each customer segment wants. And don’t push prospects to where they don’t want to go. Get the deal done.
And lastly, be flexible in the beginning. Once you have a brand that customers trust, more will prepay annually. In the beginning, at a minimum, you may need to do more paid trials and proofs-of-concepts, or quarterly/monthly deals. I know no one likes POCs and such, but you have to earn your right to skip them.
CEO: What’s the forecast?
CRO: Same as before, $3,400K.
Director 1: How do you feel about it?
CRO: Good.
Director 2: Where will we really land?
CRO: $3,400K. That’s why that’s the forecast.
Director 1: But best case, where do we land?
CRO: Best case, $3,800K.
Director 2: How do you define best case?
CRO: If the stars align.
Not very productive, is it?
I’ve already blogged about one way to solve this problem: encouraging your CRO think probabilistically about the forecast. But that’s a big ask. It’s not easy to change how sales leaders think, and it’s not always the right time to ask. So, somewhat independent of that, in this series I’ll introduce three concepts that help ensure that we have better conversations about the forecast and ultimately forecast better as a result: triangulation forecasts, to-go pipeline coverage, and this/next/all-quarter pipeline analysis. In this post, we’ll cover triangulation forecasts.
Triangulation Forecasts
The simplest way to have better conversations about the forecast is to have more than one forecast to discuss. Towards that end, much as we might take three or four bearings to triangulate our position when we’re lost in the backcountry, let’s look at three or four bearings to triangulate our position on the new annual recurring revenue (ARR) forecast for the quarter.
In this example [1] we track the forecast and its evolution along with some important context such as the plan and our actuals from the previous and year-ago quarters. We’ve placed the New ARR forecast in its leaky bucket context [2], in bold so it stands out. Just scanning across the New ARR row, we can see a few things:
We sold $3,000K in New ARR last quarter, $2,850K last year, and the plan for this quarter is $3,900K.
The CRO is currently forecasting $3,400K, or 87% of the New ARR plan. This is not great.
The CRO’s forecast has been on a steady decline since week 3, from its high of $3,800K. This makes me nervous.
The CRO is likely pressuring the VP of Customer Success to cut the churn forecast to protect Net New ARR [3].
Our growth is well below planned growth of 37% and decelerating [4].
I’m always impressed with how much information you can extract from that top block alone if you’re used to looking at it. But can we make it better? Can we enable much more interesting conversations? Yes. Look at the second block, which includes four rows:
The sum of the sales reps’ forecasts [5]
The sum of the sales managers’ forecasts [6]
The stage-weighted expected value (EV) of the pipeline [7] [8]
The forecast category-weighted expected value of the pipeline [9]
Each of these tells you something different.
The rep-level forecast tells you what you’d sell if every rep hit their current forecast. It tends to be optimistic, as reps tend to be optimistic.
The manager-level forecast tells you how much we’d sell if every CRO direct report hit their forecast. This tends to be the most accurate [10] in my experience.
The stage-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each stage. A $1M pipeline consisting of 10 stage 2 $100K oppties has a much lower EV than a $1M pipeline with 10 stage 5 $100K oppties — even though they are both “$1M pipelines.”
The stage-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each forecast category, such as commit, forecast, or upside.
These triangulation forecasts provide different bearings that can help you understand your pipeline better, know where to focus your efforts, and improve the accuracy of predicting where you’ll land.
For example, if the rep- and manager-level forecasts are well below the CRO’s, it’s often because the CRO knows about some big deal they can pull forward to make up any gap. Or, more sinisterly, because the CRO’s expense budget is automatically cut to preserve a target operating margin and thus they are choosing to be “upside down” rather face an immediate expense cut [11].
If the stage-weighted forecast is much lower than the others, it indicates that while we may have the right volume of pipeline that it’s not far enough along in its evolution, and ergo we should focus on velocity.
Now, looking at our sample data, let’s make some observations about the state of the quarter at SaaSCo.
The reps are calling $3,400K vs. a $3,900K plan and their aggregate forecast has been fairly consistently deteriorating. Not good.
The managers, who we might notice called last quarter nearly perfectly ($2,975K vs. $3,000K) have pretty consistently been calling $3,000K, or $900K below plan. Worrisome.
The stage-weighted EV was pessimistic last quarter ($2,500K vs. $3,000K) and may need updated probabilities. That said, it’s been consistently predicting around $2,600K which, if it’s 20% low (like it was last quarter), it suggests a result of $3,240K [12].
The forecast category-weighted expected value, which was a perfect predictor last quarter, is calling $2,950K. Note that it’s jumped up from earlier in the quarter, which we’ll get to later
Just by these numbers, if I were running SaaSCo I’d be thinking that we’re going to land between $2,800K and $3,200K [13]. But remember our goal here: to have better conversations about the forecast. What questions might I ask the CRO looking at this data?
Why are you upside-down relative to your manager’s forecast?
In other quarters was the manager-level forecast the most accurate, and if so, why you are not heeding it better now?
Why is the stage-weighted forecast calling such a low number?
What’s happened since week 5 such that the reps have dropped their aggregate forecast by over $600K?
Why is the churn forecast going down? Was it too high to begin with, are we getting positive information on deals, or are we pressuring Customer Success to help close the gap?
What big/lumpy deals are in these numbers that could lead to large positive or negative surprises?
Why has your forecast been moving so much across the quarter? Just 5 weeks ago you were calling $3,800K and how you’re calling $3,400K and headed in the wrong direction?
Have you cut your forecast sufficiently to handle additional bad news, or should I expect it to go down again next week?
If so, why are you not following the fairly standard rule that when you must cut your forecast you cut it deeply enough so your next move is up? You’ve broken that rule four times this quarter.
Once I complete this three-part series, I will put a link to the spreadsheet here and a link to the next post in the series here.
# # #
Notes
[1] This is the top of the weekly sheet I recommend CEOs to start their weekly staff meeting.
[2] A SaaS company is conceptualized as a leaky bucket of ARR.
[3] I cheated and look one row down to see the churn forecast was $500K in weeks 1-6 and only started coming down (i.e., improving) as the CRO continued to cut their New ARR forecast. This makes me suspicious, particularly if the VP of Customer Success reports to the CRO.
[4] I cheated and looked one row up to see starting ARR growing at 58% which is not going to sustain if New ARR is only growing at ~20%. I also had to calculate planned growth (3900/2850 = 1.37) as it’s not done for me on the sheet.
[5] Assumes a world where managers do not forecast for their reps and/or otherwise cajole reps into forecasting what the manager thinks is appropriate, instead preferring for managers to make their own forecast, loosely coupling rep-level and the manager-level forecasts.
[6] Typically, the sum of the forecasts from the CRO’s direct reports. An equally, if perhaps not more, interesting measure would be the sum of the first-line managers’ forecasts.
[7] Expected value is math-speak for probability * value. For example, if we had one $100K oppty with a 20% close probability, then its expected value would be $100K * 0.2 = $20K.
[8] A stage-weighted expected value of the (current quarter) pipeline is calculated by summing the expected value of each opportunity in the pipeline, using probabilities assigned to each stage. For example, if we had only three stages (e.g., prospect, short-list, and vendor of choice) and assigned a probability to each (e.g., 10%, 30%, 70%) and then multiplied the new ARR value of each oppty by its corresponding probability and summed them, then we would have the stage-weighted expected value of the pipeline. Note that in a more advanced world those probabilities are week-specific (and, due to quarterly seasonality, maybe week-within-quarter specific) but we’ll ignore that here for now. Typically, one way I sidestep some of that hassle is to focus my quarterly analytics by snapshotting week 3, creating in effect week 3 conversion rates which I know will work better earlier in the quarter than later. In the real world, these are often eyeballed initially and then calculated from regressions later on — i.e., in the last 8 quarters, what % of week 3, stage 2 oppties closed?
[9] The forecast category-weighted expected value of the pipeline is the same the stage-weighted one, except instead of using stage we use forecast category as the basis for the calculation. For example, if we have forecast categories of upside, forecast, commit we might assign probabilities of 0.3, 0.7, and 0.9 to each oppty in that respective category.
[10] Sometimes embarrassingly so for the CRO whose forecast thus ends up a mathematical negative value-add!
[11] This is not a great practice IMHO and thus CEOs should not inadvertently incent inflated forecasts by hard-coding expense cuts to the forecast.
[12] The point being there are two ways to fix this problem. One is to revise the probabilities via regression. The other is to apply a correction factor to the calculated result. (Methods with consistent errors are good predictors that are just miscalibrated.)
[13] In what I’d consider a 80% confidence interval — i.e., 10% chance we’re below $2,800K and 10% chance we’re above $3,200K.
Let’s cut to the chase folks… chatbots have struggled to gain much of a foothold in the corporate tech innovation stack. However, our analyst Melissa O’Brien has spent the last few years studying how these engagement technologies are evolving deeper into the enterprise where they can truly augment staff and reduce a significant amount of their time while driving a whole new digital way of working for many enterprises. So let’s take a look at which services firms are getting good at creating these digital associate workers for enterprises…
Melissa, we’ve been observing the evolution of “conversation AI” for a good decade-plus now, so what’s new? has the pandemic driven more uptake?
The demand for conversational AI has exploded over the last year. The automation tools we call digital associates were one of the digital superheroes of the pandemic, as conversational tools picked up the slack in handling volumes of interactions when human associates were not available due to a lack of work-from-home preparedness. Reduction in staff coupled with spike in volumes of interactions in many industries such as ecommerce created a burning platform. For many companies, this rapid and massive disruption resulted in accelerating digital initiatives already in play and pushing many lingering POCs into production mode. Industries like travel and hospitality, which had a brief period of an incredible uptick in customer interaction volumes a year ago, have now developed conversational tools poised to address inevitable future surges driven by pent-up demand.
Now that acceptance and adoption has increased significantly, the imperative is to move beyond the low-hanging fruit, the really simple and repetitive stuff, to really see what these tools are capable of. The next evolution of DA’s is a lot more about using ML and AI for conversational complexity, having a good design that fits into an overall experience strategy, and integration with enterprise systems in order to deliver greater value. Many companies which hadn’t dipped a toe into this space pre-pandemic are struggling a bit with the learning curve now, where others that had a foundation built already are expanding their use cases. Enterprises are now really looking at digital associates from an employee and customer experience perspective rather than only call deflection, efficiencies, and cost savings. In many cases, we’ve seen them as effective tools for sales conversions and building brand loyalty. So there are a lot more potential outcomes that companies are now looking to conversational AI to help achieve.
Why do you think this space has struggled to attract the feverish hype of RPA and “dumb” back office automation? What needs to happen to get the digital associate value proposition weaved into the whole intelligent automation and AI narrative?
I think part of the trouble is this market landscape is vast, the tools poorly defined and buyers are confused. This is part of the reason we’ve taken to calling the tools “digital associates,” to convey that they are digital workers built to provide a tangible outcome. Digital associates are not distinct segments like AI, automation, and analytics – in fact, they are tools that if designed properly use elements of all three to maximize their power. The face of automation has been largely tied to RPA, a singular tool where a lot of the use cases are straightforward – the quick time to value in many of these tools makes it a fairly fast and easy win for certain processes. With conversational AI, there’s a lot more nuance to think through employee and customer journeys, the complexity that is human language and interaction, and the connections that need to be made in disparate back end systems for them to operate in a meaningful way at the engagement layer.
Digital associates run the gamut of maturity, but often get associated with their simplest cousin, the chatbot. Chatbot fatigue has been a real roadblock; 10+ years ago the fad was to slap an FAQ bot on every website, expecting it to deflect calls and create efficiencies. More often they provided little help outside of website navigation, and created customer frustration and degraded the experience in the process. So people who hearken back to those experiences will have an aversion to trying out conversational tools or be hesitant about their value. This is obviously changing now as the real digital associates are starting to prove their worth.
I think this is where the OneOffice helps people conceptualize the impact digital associates can have on an organization. Firstly, start with an EX or CX focused outcome objective and design the DA backwards from there. As you’ve said yourself Phil, digital associates are augmenters of the human experience. They start to add more value as they become more sophisticated and embedded in our processes. And thinking in terms of the Triple-A Trifecta, the digital associates’ AI-powered brain is complemented by automation arms and legs to retrieve, report, update and transact. Data is the fuel the associate ingests to produce insights (analytics) and achieve optimal performance, including the personalization users are expecting.
And which suppliers are currently winning in this market, Melissa? Both in terms of services and products?
Accenture, IBM and Cognizant took the top three spots respectively in our recent services Top Ten. These providers demonstrated an impressive breadth of capabilities across industries and enterprise functions, both IT and business, and a depth of design, process and technology prowess. While we didn’t include advisory firms in our assessment, focusing instead on BPO and IT services implementations, firms like KPMG and Deloitte have significant depth of expertise in developing solutions for clients in this area also.
On the products side, the number of vendors having some level of capability in this space is astounding. You’ve got the cloud providers which all have their platforms and developer suites, namely Google Dialog Flow & CCAI, AWS Lex & Connect , Microsoft Luis & Power VA. And then your established OOB software firms like IPsoft and Kore.AI, and including some of the RPA firms such as Pega and NICE dabble in various forms of digital associates. And we’ve been watching some interesting up and comers like last year’s “hottest hot vendor” Techforce.ai, and most recently we’ve been briefed on XpertRule’s Viabl.ai platform which comes in with an interesting value proposition focused on decisioning.
We’re really seeing this as an ecosystem play right now, as most of the successful implementations we’ve seen have required fairly complex customizations and integration from the service providers. While the products players tend to focus on selling licenses, the service providers provide with industry knowledge, deep understanding of clients’ processes and design expertise to help companies think through employee or customer touchpoints and really map out what they want to accomplish. And most of the service providers are completely technology agnostic, so they can come in and leverage an existing vendor relationship or preference or help evaluate vendors and select based on their requirements.
How are the call center firms approaching digital associates – are they reacting to customer demand, or still fighting to protect the legacy “butts-on-seats” model? Which ones are unafraid to disrupt the model and push conversation AI aggressively?
Many legacy contact centers still operate on FTE volumes and they’re safe for now because there’s still plenty of demand for labor-focused services (which is being driven quite a bit by growth in digital native companies whose rapid growth requires help to scale operations.) But most of the contact center leaders have a balanced approach which strategizes with clients on digital-first customer journeys which include a blend of automation and agent support. Concentrix is one of the most aggressive we’ve seen in this market, which landed at #9 in our recent report, the top ranking of any of the contact center pure plays. The challenge that the CX players continue to have is that of brand perception. They know that with the increase in self-service and automation that in many client engagements, if they don’t cannibalize their business another provider or vendor will. So they’ve built solid AI and automation capabilities for those clients with the appetite to work with them in that regard. The trouble is that some enterprise tech leaders won’t consider a contact center to work with on emerging tech, so in order for them to really gain traction in this space, there’s a mindshare gap to bridge, especially with IT decision-makers.
And finally, Melissa, how do you expect digital associates to evolve as we get used to new ways of working? are they going to branch way beyond customer-facing solutions?
Customer service examples are the most ubiquitous, but digital associates have already spread well beyond that realm. In the top ten, we saw a plethora of use cases in procurement, HR, IT helpdesk, finance, accounting, and much more. One of the most prevalent examples we found was the prevalence of recruitment and HR bots. We’ve seen conversational AI tools which can help shepherd a candidate through the application and interview process, and also offer employee support once onboarded, helping staff access, understand, and process important information around benefits and compensation, performance, and time reporting. This is really a result of how accustomed we’ve become to automation in our consumer lives, that we expect to interact with digital ‘colleagues’ at work. And while chat solutions are still very commonplace, I do think the future of this market lies in voice. Not phone-based or intelligent IVR’s, but similar to virtual assistants like Alexa and Siri we’ve become so accustomed to in our consumer lives. Voice is opening so many doors right now to create new ways for people to engage either with our work as employees or with companies to buy services and products.
Many of the examples we saw were also quite industry-specific solutions, and I think we’ll continue to see greater depth in those capabilities too. Personalized menu customization for restaurant chain ordering, retail loan processing, and approval, program enrollment and Rx management for healthcare payers, and retail order status digital associates were just a few of the highly verticalized solutions we’ve seen.
One of the important topics I expect to see the industry tackle next is how these tools are supporting diversity and inclusion efforts. For example, service provider TTEC has developed a bot as part of a broader D&I offering which allows employees to ‘practice’ difficult conversations, around prejudices regarding race or gender for example, with the conversational AI tool. The platform then provides feedback and advice on how to handle the conversation. Another big one to unpack is how these tools play a role in influencing and in many cases reinforcing gender bias (did you know that “female” digital associates get greater adoption levels than male ones?). We recently saw Accenture coming out with a non-binary offering as an option to address this. I think we’ll see a lot coming from the top service providers which frame digital associates in the light of “ethical AI.” How to ensure an ethical approach an important conversation to have, because emerging and increasingly sophisticated conversational tools will help shape our future reality, from the fun and entertaining, to potentially serious and unnerving. The conversation is an extremely complex AI problem to solve, but with all the opportunities we’re seeing emerge, it feels like this is just the beginning of exploring the possibilities.
HFS premium subscribers can access the Digital Associates Top Ten report by clicking here
LinkedIn is relying on a new methodology for the 2021 Top Companies Report. They’re basing the methodology has seven key pillars, each revealing an important element of career progression: the ability to advance, skills growth, company stability, external opportunity, company affinity, gender diversity, and educational background. LinkedIn provides an in-depth description of how they built their methodology here.
The 10 Best Companies To Grow Your Career In 2021
Amazon – According to LinkedIn, Amazon has built an innovative remote-onboarding system, and it has more than 30,000 openings now. The fastest-growing skills in demand at Amazon include User Experience Design (UED), Digital Illustration, and Interaction Design. LinkedIn’s analysis shows the most in-demand jobs are Health And Safety Specialist, Station Operations Manager, Learning Manager.
Alphabet, Inc – Planning to add at least 10,000 jobs in the U.S. alone and investing $7B in data centers and offices across 19 states, Alphabet grew revenue 47% last year, reaching $13B. According to LinkedIn, the most in-demand jobs are Digital Specialist, Field Sales Specialist, and Business Systems Analyst.
JPMorgan Chase & Co. – JPMorgan now offers 300 accredited skills and education programs to its workers, and the bank has been boosting wages for thousands of customer-facing roles to $16-$20 an hour. The most in-demand jobs include Market Specialist, Software Engineering Specialist, and Mortgage Underwriter.
AT&T – 2020 was a tough year for AT&T, increasing the urgency the company has to grow its wireless and WarnerMedia businesses. Due to the pandemic, the company had to close hundreds of stores. Fortunately, AT&T was able to help the employees affected by the closures to find new jobs. The most in-demand jobs are Service Analyst, Trading Analyst, and Investment Specialist.
Bank of America – Bank of America rose to the challenges of 2020, quickly redeploying almost 30,000 employees to assist in its role facilitating the government-backed Paycheck Protection Program. The most in-demand jobs are Trading Analyst, Investment Specialist, and Financial Management Analyst.
IBM – More than one-third of IBM’s revenue now comes from work related to cloud computing. The company’s Red Hat unit is a leading contributor to that growth, prizing skills such as Linux, Java, Python, and agile methodologies. IBM also is a leader in hiring autistic people through its Neurodiversity program. Most in-demand jobs include Back End Developer, Enterprise Account Executive, and Technical Writer.
Deloitte – Deloitte’s key activities span audit, assurance, tax, risk, and financial advisory work, as well as management consulting. It’s aiming to hire 19,000 people in the year ending May 29. Top recruiting priorities currently include cybersecurity, cloud computing, and analytics specialists.
Apple – LinkedIn finds that Apple is committed to building an inclusive culture. Over half of its new hires in the U.S. represent historically underrepresented groups in tech — and the company claims to have achieved pay equity in every country where it operates—looking for an in? Apple has nearly 3,000 open jobs in the U.S. right now, ranging from its “genius” role at its retail stores to executive assistants and software engineers.
EY – The accounting firm spent $450 million on employee training in 2020. And it is planning to hire over 15,000 people in the next year. With that much talent coming in, EY is focused on bringing in workers with diverse backgrounds, focusing on gender identity, race, and ethnicity, disability, LGBT+, and veterans. The most in-demand jobs include Strategy Director, Business Transformation Consultant, and Information Technology Consulting Manager.
Bottom Line: Customer revenue lifecycles are the lifeblood of any services business, making FinancialForce’s Spring 2021 release timely given the services-first revenue renaissance happening today.
The essence of an excellent services business is that it can consistently create expectations clients trust and the business regularly exceeds. Orchestrating the best people for a given project at the right time, tracking costs, revenue, and margin across all services revenue, including those associated with a client’s assets, is very challenging. Customer revenue lifecycles are in the data, yet no one can get to them because they’re hidden across multiple systems that aren’t integrated. Knowing how efficient a services business is at turning customer engagement into cash is what everyone needs to know, but no one can find. The challenge is equally as daunting for long-established services providers and those rushing into new services businesses to redefine themselves in the hope of profits that are more consistent and fewer price wars.
How Much Is Customer Engagement Is Worth?
Services businesses face the paradox of exceeding client expectations with every engagement but not knowing if extra time, resources, and staff invested are paying off with more revenue and profit. FinancialForce’s Spring 2021 release looks to solve this problem. What galvanizes the ERP, PSA, and platform announcements is a fresh intensity on customer centricity, both for the services business adopting the Spring 2021 release and the customers it’s intended to serve.
Knowing if and by how much a given customer engagement and its revenue lifecycle generate cash, and its potential is one of the core focus areas of the Spring 2021 release. It’s badly needed as many services are flying blind today, overcommitting resources for little return and too often losing control of client engagement and paying the price in lost margin and profits. FinancialForce sees that pain and wants to alleviate it with better financial visibility on all aspects of customer services revenue. FinancialForce aims to provide customer-centric financial reporting down to the revenue stream and costing measure level.
Knowing every customer’s impact on revenue and profitability from all revenue streams will make managing services engagements much more accurate, easier to manage, and more profitable.
Key Takeaways From The Spring 2021 Release
Customer centricity seen through a financial lens is the cornerstone of FinancialForce’s latest release. One of the primary goals of this release is to update more applications to Salesforce Lightning to provide FinancialForce users with a more consistent user experience across all applications. Salesforce has been doubling down for years on Lightning and its user experience technologies, with FinancialForce reaping the benefits for over a decade. FinancialForce is transitioning their core Professional Services Automation (PSA), Billing, Accounting & Finance and Procurement, Order and Inventory Management to Lightning in this release in response to their customers wanting a consistent user experience across the entire FinancialForce suite of applications. The Spring 2021 release reflects how FinancialForce strives to provide a real-time understanding of customer lifetime value for their ERP and PSA customers.
Additional key takeaways include the following:
FinancialForce sees reducing days to close as one of the highest priorities they need to address today. The majority of new feature announcements center on how the days to close cycles can be streamlined, especially across multi-company and multisite locations across geographic and currency-specific regions of the world. Multi-company currency revaluation will help FinancialForce customers who operate across multiple geographies that operate in different currencies and will be especially useful for those clients creating new global channels and considering foreign acquisitions. Further showing the high priority they are putting on reducing days to close, the Spring 2021 release also includes automated eliminations, multi-company period close for software closes, which are designed to temporarily close out a financial report and revenue schedules that can provide a future view in revenues – a key factor in knowing customer revenue lifecycles.
New features and a new Lightning interface for Accounting, Billing Central, and Inventory Management simplifies complex transactions for users. FinancialForce has one of the most customer-driven product management teams in enterprise software. The depth of features they have added to inventory management, transactional and reconciliation processes for accounting, drop-ship use cases, and enhancements for adding products to billing contracts show how much FinancialForce is listening to customers.
AI-enhanced financial reporting that works with any Einstein data set. FinancialForce leads the Salesforce partner ecosystem when it comes to integrating Tableau CRM (formerly known as Einstein Analytics) into its platform. Now thirteen releases in, FinancialForce’s Spring 2021 release reflects the intuitive, adaptive intelligence that the product management team aims to achieve by integrating Einstein into their financial reporting workflows.
Professional Services Automation (PSA) Applications Including Resource Management, Project Management, and Time & Expense upgraded to Lightning. Transitioning three of the core PSA applications to Lightning will help broaden adoption and make them easier to upsell and cross-sell across the FinancialForce customer base. It will also help existing customers using these applications get new employees up to speed faster on them, given how much more streamlined Lightning is as an interface compared to previous versions.
Intelligent Staffing solves the complex challenges resource managers face when assigning the best possible associates to a given project. Designed to filter and intelligently rank potential resources based on region, practice, group skill sets, and availability, Intelligent Staffing is designed to get resource managers as close to an ideal match as possible for a given project’s requirements. This is a much-welcomed new feature by FinancialForce customers who are large-scale services providers as they’re facing the challenges of assigning the right person to the right project at the right time to ensure project success.
Integration of Salesforce AI’s Next Best Action (NBA) will raise the level of project expertise at scale across customers. Part of the customer centricity focus in Spring 2021 is focused on providing customers with new technologies and applications to share expertise and knowledge at scale. Next Best Action provides prescriptive guidance for the project manager and will see heavy use in new associate onboarding across services businesses and achieve greater corporate-wide learning at scale. This is consistent with the focus in the Spring 2021 release on bringing greater space and speed to mid-size and larger services customers.
Conclusion
FinancialForce defines customer engagement and centricity from a financial standpoint in the Spring 2021 release. Too often, services businesses commit to large-scale projects without a clear idea of the customer revenue lifecycle. With FinancialForce, they can stop and ask if the level of customer engagement they’re committing to is worth it or not – and if it isn’t, what needs to be done. FinancialForce is doubling down on user experience and accelerating time-to-close, two areas their customers want innovation to and look to them to deliver. Look for FinancialForce to scale out with more MuleSoft and Tableau integration scenarios, all aimed at capitalizing on their expertise developing on the Salesforce platform. There’s a bigger challenge to customer engagement on the horizon, and that’s providing a real-time view of financials across all customers with all available data across a business, making MuleSoft integration key to FinancialForce’s future growth.
In the 39th (yesterday) and 40th (today) episode of the series we have Phil Wainewright of Diginomica and Jeff Kaplan of THINKstrategies, two of the pioneering influencers in the SaaS/cloud marketplace.
In part 2 below we talk about how business models are evolving and how servitization has become a significant trend in every industry vertical, not just in enterprise technology – as Phil calls it Everything as a Service.
In part 1 yesterday we had taken a walk down memory lane on how the SaaS, cloud infrastructure and platform economies have evolved over the last 25 years.
Google Cloud Has Hired A Brain Trust Of Industry Vertical Leaders
On March 31st, 2021, Google Cloud hosted an industry vertical analyst roundtable. Google Cloud CEO, Thomas Kurian, opened up the day with a warm introduction on the role industries will play in the growth of Google Cloud. Lori Mitchell-Keller, VP of Industry Solutions shared how Google Cloud differentiates its approach to Industries with customers. Analysts were introduced to the key industries and leaders for Google Cloud’s Verticals whom include:
Customer Wins Range From Level 1 to Level 4 Digital Giant Partnerships
Google Cloud’s mid-term goal is to build Level 4 partnerships with industry leaders. Key focus areas for each of the verticals include
Data and AI platforms
Pervasive AI-enabled industry solutions
Cross-functional digital ecosystems
Specific plans for each vertical include:
CPG – know customers in a data privacy first world, deliver growth in an omni channel ecosystem, and enable a more intelligent sustainable operations
FSI – humanize digital experiences, modernize core technologies and drive out technical debt, adjust to a deluge of regulatory requirements, and manage risk through data
Health and Life Sciences – accelerate R&D and clinical insights, achieve precision decisions with ML/AI, and enable population health and value based care models
Manufacturing – digitize customer journeys to improve experience through data, optmiize operations and sustainability, and address cultural and technology needs for digital transformation
Media and entertainment – drive up content velocity, deliver mass personalization at scale, ad create dynamic feedback loops to improve relationships and context
Public Sector – deliver compliance without compromise, provide tools to accelerate digital transformation, solve for mission needs, and apply vast public data troves for public good.
Retail – focus on immersive and frictionless customer experiences using first party data to accelerate activation, and decrease cost of business
Supply Chain and Logistics – deliver exceptional customer experience, drive circular economy and sustainability, achieve autonomous enterprise status
Telco and CSPs – identify new digital monetization, improve low touch high value experiences, upgrade legacy technical debt and core systems, and upskill work force.
While most of the customers are focused on Level 1 and Level 2 capabilities, Google Cloud is starting to see growth in Level 3 vertical industry wins and becoming more competitive with both Amazon Web Services and MIcrosoft Azure in industry deals (see Figure 1). Public wins at Amwell, Ford, MLB, Target, Unilever, Unity, and Verizon are proof points on the extent of the partnerships with industry for data-driven digital transformation.
Figure 1. The Five Levels Of Cloud Provider Partnerships
The Bottom Line: Build, Partner, or Perish
In my upcoming book, Everybody Wants To Rule The World, we discuss how the collapse of vertical industries along a convergence of value chains will lead to 100 companies in 50 value chains across seven major geographic trade zones by 2050. Established organizations who survive to compete against the next set of well-funded digital giants will have to create joint venture partnerships in Level 5 in order to succeed.
Given the capital constraints of the established industry leaders, most will have to partner instead of build. Many of those partnerships will occur with the major cloud platform vendors who can provide the investment capital, the technical expertise, and the key cloud computing and technology prowess needed to win. Digital leaders who plan for the future will have to double down on one to two cloud players across the world to build deep partnerships or invest in the capital to establish their capabilities. Winners will build or partner.
A start-up leader told me the other day that they didn‘t want to go into a market category because they reckon the big established vendors own that space. I told them they were wrong. Sure the big vendors are very successful there, but no one owns a market category in perpetuity. They were believing the vendor marketing more than the customers were.
The marketing machine at the big vendors beats a loud drum, and with many big vendor employees retweeting and amplifying press releases and case studies, they can seem totally dominant, especially on Twitter and on Linkedin. But most of the time they aren’t nearly as omnipotent as you may think. They have problems and challenges that start-ups don‘t have, and twitter is not the real world.
I‘m reminded of a post by Jason Lemkin from a couple of years ago. (If you work in SaaS and you don’t read Jason, you should. Attend his events too).
“But what we can say is that about 70% of public SaaS companies are applications that are new versions, new entrants, in well-established categories
Big doesn’t always mean powerful
Many start ups often see big vendors as unassailable. “How can we compete with their marketing?” But big vendor customers are now largely immune to airport posters, massive conferences and bland marketing campaigns with actor voice overs.
Many start ups imagine that big vendors have huge budgets and R&D innovation labs waiting to be deployed to repel the new incumbents. Heck, agreeing a budget for hiring a new team In a big company usually takes far longer than that it takes for a start up with a half coherent idea to raise a seed round. Inertia is a really powerful force in large companies.
They often imagine that big companies have access to the latest and coolest technologies. Big companies rarely do, instead they have decades of technical debt, and the design decisions of their predecessors to contend with.
The large vendor sales machines are powerful, but generally lack specialisation, and don’t always work so well at the department level sale.
Not all products are made or sold equally
Also while the incumbent, large vendors have broad product portfolios, not every element of the portfolio has strong adoption. Sometimes vast market opportunities exist in the most surprising spaces.
Over the last decades ERP vendors have poured huge R&D efforts into employee portal capabilities. ServiceNow has also grown massively, and Microsoft Sharepoint is almost ubiquitous as an intranet platform, Teams and Slack are now everywhere. Every company has some sort of employee portal. Given this, you would be forgiven for thinking there wasn’t a market for specialised tools to help with employee communications. Yet recently, Staffbase, the employee communications vendor based in Chemnitz in Germany, raised 145 million to expand its employee communications offering. Its customers include companies such as Adidas, Audi, BHP, Deutsche Post DHL, Groupon, Hitachi, Ikea, Johns Hopkins University, McKesson, Paulaner, Suncor, Viessmann, and Volvo, who are all massive large organisations who have already invested heavily in the products from the larger vendors. In a similar vein, Lumapps, out of Paris, raised 70 million last year.
HR Tech is littered with examples of incumbent disruption. Even the most incumbent friendly application of all, payroll, is seeing massive disruption. New ideas, new technologies, and massive funding destroys the age old moats and walls..
Advice for start ups
Respect the big vendors, but do not fear them. Solve a compelling problem better than they do and work out how to reach your buyer. Do this and you will do very well, incumbent there or not. It has never been easier to disrupt the status quo than today. This is why over 2,5 billion USD of VC money flowed into HRTECH in the first quarter of 2021 alone.
(the photo is of Heidelberg Castle. Author’s iphone 12 pro snap. History here)
