How SaaS Companies Use Predictive Analytics for B2B Sales & Marketing
The customer is central to SaaS success. Even the best SaaS product relies on rapid customer uptake to scale, and continued customer loyalty for profit. Add high acquisition costs and tight margins and you get a narrow line between success, flounder and fail.
All SaaS businesses ask the same questions. How do we:
- Identify and target our total addressable market without wasting money on dud prospects?
- Progress leads through to sale cost-effectively?
- Maximise customer satisfaction, retention and lifetime value without amping up spend?
Predictive helps answer those questions, by adding machine learning to your existing processes to make your activity more effective and efficient. Let’s look at how.
1. Identify Ideal Customer Profiles (ICP) and Total Addressable Market (TAM)
A major challenge for SaaS organisations is identifying the maximum number of ideal prospects to support rapid growth. Identifying your ideal customer profile and defining your total addressable market. In other words, you want to understand where demand exists – and where it doesn’t – so your prospecting is as efficient as possible.
Right now, most organisations rely on basic lookalike profiling based on outdated firmographic data to find similarity to existing customers. The problem is, this method is very crude with broad classifications that span different customer behaviours and values.. So you end up targeting general segments with generic messaging.
And marketers know that. We know it’s imperfect, but the alternative is a costly new account data acquisition strategy and further ‘spray and pray’ approach that isn’t justifiable or feasible.
At least, that’s been the only alternative until now.
Advanced predictive builds a sophisticated ideal customer profile based on behavioural segmentation. The technology assesses hidden signals that show intent and fit, rather than the standard factors like industry, size, revenue and geography.
It works by analysing your existing internal customer data, which is enriched with external data (so it’s not a disaster if your internal data is less than fantastic). Then predictive builds a complex understanding of what makes your customers your customers. That’s your ideal customer profile.
Then you get a comprehensive prospecting list of your total addressable market, ranked in priority order. You know who to target for your solution: that’s the first hurdle.
2. Increase conversion rates with predictive lead scoring
Understanding your total addressable market is one thing. Drawing those prospects into and through your sales funnel is quite another. How do you know where to focus? Where’s the sweet spot, where your investment will deliver maximum returns? How do you know which leads should go to sales, and which need email nurturing?
If you’re familiar with the recent SiriusDecisions Demand Unit Waterfall, you’ll know they talk in terms of Active Demand, Engaged Demand, Prioritised Demand and Qualified Demand.
Whatever you call it, the principle is the same: understanding prospect demand throughout the sales funnel. The better we do that, the better we can serve those leads/customers – because we know what they’re ready to hear and when.
When you understand your prospects you can personalise your messaging based on that understanding. Which means they’re more likely to be receptive and convert (whether that’s freemium or another model altogether). It’s about delivering the right messages to the right people on the right channel, at the right time. Pulling prospects more effectively through the sales cycle.
Predictive analytics plays a major role with predictive lead scoring. Analysing the historic signals that indicate lead intent at each stage of the customer lifecycle (or SiriusDecisions waterfall), the technology can then predict how future leads will behave.
Which means instead of an amorphous list of potential prospects, you get a narrow list ordered by who’s most likely to convert and when, and their lifetime value. So you can focus activity and optimise your channel strategy accordingly. Nurturing leads that need nurturing; selling to leads who’re ready to buy.
Companies using predictive have benefitted from:
- 5x more sales converted via internal sales (vs manually selected group)
- 6x more sales via email nurture campaigns (vs held back control group)
- 32% saved effort on lead follow up
3. Identify customers most likely to leave or become inactive
Retention is especially important to SaaS businesses given the snowball effect of customer longevity. Improve retention to increase customer lifetime value to improve revenue and increase cash to drive more future growth: it’s a well-worn equation.
The problem is identifying which customers might leave or become inactive early enough so you can intervene and stop them. Without that information, any customer support/success team is working behind the curve.
That’s where predictive analytics platforms help. Advanced predictive zooms in on your historic customer data to understand why past customers have left/become inactive / reduce spend and which warning signals they give before they do (including, but definitely not only, usage).
From that, the technology forms a complex picture of what flight-risk/inactivity risk customers look like, which it then applies to your existing customers.
You get an accurate prediction of which customers are likely to leave or become inactive – before they know, even. You can then target them with the appropriate campaign to influence their future behaviour. And the platform continuously learns from itself, becoming more accurate the more you use it.
For instance, BrightTarget’s platform can generally predict subscription churn three months in advance at 80%+ accuracy, with improvements from there.
This empowers you to focus retention activity in the most efficient, effective way to increase customer lifetime value. It’s what you already do but without the guesswork.
4. Predict and prioritise by customer lifetime value
Customer lifetime value, CLV, is a hugely important metric for SaaS businesses. But what if, instead of reporting CLV, you knew the future CLV of prospects, leads and customers – as soon as they were on your radar?
That’s what advanced predictive analytics can do. And it’s something we’re especially well-placed to talk about, as the Q2 2017 Forrester Wave report, Predictive Marketing Analytics for B2B Marketers, proves: “BrightTarget’s star rises by focusing on customers’ lifetime value”.
So how does it work? The technology looks at thousands of internal and external data points to understand how data signals correlate to customer value. It then predicts the future value of prospects, leads and customers – so you can focus activity to maximise CLV.
“Incorporating unique information, such as invoice history, service records, and calculated cost to serve, BrightTarget helps business executives and marketers calculate their customer base’s current value. It then determines which steps to take with customers or prospects to maximize business equity”.
Combining this information on CLV with likelihood to convert gives you a clear matrix for prioritisation.
You can send your A1 prospects straight to sales while B2 prospects go to marketing for further nurturing. You can cull C3 prospects completely, so you’re never wasting time in the wrong places.
Don’t be deceived by the name, because predictive marketing analytics definitely isn’t just for marketers. Sales love predictive, because they can spend time on genuinely sales-ready leads that are more likely to convert. It really is win, win.
5. Drive existing customer growth through better upsell and cross-sell
Existing customer growth is a major use case for predictive because it helps you understand which prospects to upsell/cross-sell to, and when. So you can maximise existing customer growth, but also you can better serve your customers. Which has a knock-on impact on customer satisfaction, retention and lifetime value.
Right now, SaaS organisations often miss those opportunities. Say ten customers in a global organisation sign-up for your product. If you could easily spot that, you could make an upgrade offer for the enterprise – everyone’s happy.
Or imagine your sales team are about to close a global deal through decision-makers in one territory, but you start prospecting into another territory in the same account. You risk the deal, step on sales’ toes and frustrate your customer. Nobody’s happy.
Predictive gives you a single customer view across departments, products, services and territories, so you can work from a more complete picture. It’s a bird’s eye perspective that makes everyone more effective and valuable.
Combine that single customer view with the behavioural segmentation and intelligent prioritisation we’ve discussed, and you can deliver timely, targeted upsell and cross-sell offers to customers. That’s how you drive customer growth, improve retention and do wonders for internal politics too.
Predictive fuels the SaaS customer model
Predictive analytics is a no-brainer for SaaS businesses who want to amplify the success they’re already having. From identifying prospects onwards throughout the customer lifecycle, predictive empowers you to add more value by delivering a personalised experience that better meets customer needs.
Predictive empowers both sales and marketing to become more efficient and effective, and stop treading on one another’s toes. To the ultimate benefit of the customer experience. To the ultimate benefit of your bottom line.
Find out how predictive analytics could work for your organisation with the BrightTarget predictive marketing accelerator. This fixed-cost opportunity assessment is designed to prove the ROI potential of predictive using your own data, so you can see the value of predictive in action.
About Glen Westlake
Glen successfully founded and managed a SaaS company from start-up, through scale-up and into exit, so he knows what it takes to manage and grow a SaaS company and the commercial metrics required end to end.