Hidden revenue: Tactics to find cross-sell pipeline in your customer base
Whether your company grows beyond a single product by building new products or acquiring existing ones, offering multiple products means you have cross-sell opportunities among your customers.
If you’re in the acquisition scenario, you already know that pressure to find synergies to prove ROI on the purchase can be very high.
Discovering the opportunities is challenging, however, especially if you want to automate the process.
Here’s why and what you can do about it.
The Challenge: What signals indicate cross-sell readiness?
First, let’s distinguish between upsell and cross-sell opportunities. Upsell generally means selling a higher tier of features or more licenses for the product a customer already uses.
Using product data, finding signals that mean a customer islikely ready for upsell is fairly straightforward. You monitor the features they use, frequency of use, number of licenses, and other related data.
Unlike upsell signals, cross-sell readiness signals can be difficult to find.
Let’s look at three of the challenges.
A customer’s usage of Product A is not always directly related to their propensity to use or need for Product B.
Let’s say a company has two products.
- Product A helps recruiters find diverse employee candidates.
- Product B helps HR leaders assess bias in performance reviews of existing employees.
For a large company with a diversity and inclusion initiative, it’s logical that there would be a need for both Product A and B.
For a smaller company, hiring may be a priority but there may be no formal performance review cycle at all. This means Product A is needed but Product B may not be needed for a few years.
Finally, a third company may actually be a recruiting agency that finds candidates for other companies. It will never have a need for Product B at all.
Product usage data silos
The disconnection between products is often compounded by the fact that product usage data for each product is stored in separate locations. It’s difficult to find trends across users of one and users of the other.
Incomplete customer information
Account profiles for customers are often only partially filled (for example, you may have Industry information but not Sub-Industry), which makes it difficult to suss out types of companies that would have a need for Product A or B, or both.
The best current solution: rely on people
Given these challenges, many companies with multiple products rely almost exclusively on customer success managers to use relationships to identify cross-sell opportunities within accounts they own.
This approach is problematic for three reasons.
Separate teams have different needs
Often, additional products are not being purchased by the team that purchased the first product, so the customer success manager doesn’t have access to the new team.
Companies may be actively looking for solutions that your additional products solve, but not make us aware of it. We find out they could’ve been cross-sold to one of our products but it’s too late.
Many customer success managers are focused on the day to day responsibilities, and often cross-sell goals are tied to strategic initiatives that are not worked into daily execution.
Data-driven solution: Define cross-sell ideal customer profiles
Fear not! Data driven and automated tactics to identify cross-sell ready accounts exist.
Start by defining your cross-sell ideal customer profiles (CS ICPs). Then, monitor your data for customers who match the profiles.
Often companies have ideal customer profiles for specific products. With multiple products, you need cross-sell ideal customer profiles.
In fact, you need not just one, but rather N cross-sell ideal customer profiles based on how many products your company sells. If a company has 3 products (A, B, C), there should be four cross-sell ideal customer profiles: one for A-B, one for B-C, one for A-C, and one for A-B-C.
Each of these ideal customer profiles should be defined in 2 ways:
- Identifying the common traits of customer accounts that purchased Product A and Product B (or Product C)
- Declaring hypotheses about types of accounts that SHOULD be ideal customers of each product
Both tactics are useful, and the first can only` be done if there is historical information available for customers who have purchased multiple products.
Both tactics require rich account profiles with many descriptive traits about the accounts (such as sub-industry, size of sales team, size of HR team, detailed org charts).
Additionally, behavioral traits can also help build ideal customer profile building.
The broad categories of account enrichment that can result in accurate ideal cross-sell customer profiles are captured below.
You can choose between many sources of firmographic data (Zoominfo, User Gems, Crunchbase, Appotopia, LinkedIn etc).
Some good firmographic traits to track:
- Team size by org (sales team, marketing team, HR team, recruiting team, design team, engineering team, etc.). Many companies track customer total employee count but don’t augment this with team size. Often team size is more indicative of “ideal” customers than overall employee size.
- Current vendors/tools used by org: Again, focus on the org(s) you sell into rather than the entire company. Often a company’s usage of a specific product or category of product can indicate that they will also need a complimentary product you offer.
- Sub-industry: Often companies track high-level industry, and it’s too coarse. Adding sub-industry can help understand customers at a more refined level. For instance, instead of just Industry: Technology, having sub-industry: FinTech, Database, DevPlatform can hone in on truly ideal cross-sell customers.
Org chart & job listing scraping
Most software is used by people with specific job titles. If your Product A or Product B are such tools, scraping active job postings and current org charts of target accounts for appropriate roles can be a strong indicator that an account is viable for a specific product.
For instance, let’s say your Product A is used by lifecycle marketers, and Product B is used by event managers. A good cross-sell signal on accounts that use Product A is whether they have job titles associated with Product B (event manager).
Third party intent data
Multiple third party intent data sources, such as Bombora and Leadsift, can be used to identify existing customers who are using our Product A and actively searching for products competitive to our Product B. This data can further prioritize cross-sell ideal customer accounts to the subset that’s actively in the market.
“Linkage” product capabilities
While the usage of Product A may not be directly related to Product B, it’s helpful to do a brainstorming exercise on key product capabilities in Product A that intuitively link to a need for Product B.
For example, if Product A is a lifecycle marketing tool and Product B is a creative content creation tool, perhaps a company’s usage of Product A where many images are being embedded in email sequences suggests that Product B may be useful in the creation of that content.
No AI can replace human intuition, so we recommend using a brainstorm to generate these ideas. After that, analysis can be used to validate the correlation between the usage of a product capability in Product A and the likelihood of cross-sell to Product B.
In many cases, there’s insufficient historical data to make this analysis powerful. And that’s ok. A good hypothesis is a good place to start.
The revenue impact of data driven cross-sell pipeline
Companies that invest in this solution end up with 20-40% higher revenue per account manager.
These tactics require significant effort to set up and maintain, and the work is worthwhile for many companies. It saves account managers thousands of hours of time spent pursuing the wrong accounts and instead focuses them on the accounts that are most likely to expand to a new product line.
In today’s market conditions, we are all being pushed to do more with less. Data-driven cross-sell opportunity identification can be a game changing competitive GTM differentiator.