Analytics of B2B products

Most of the material available on the Internet in product analytics relates to B2C products. The e-Commerce industry is the leader in this field — it is online stores that are at the fore in the use of analytical tools to understand user behavior, measure conversion funnels, and UX optimization to adapt the product to the customer’s needs. For e-Commerce, we can find the most case studies, e.g., concerning the use of sales funnels.

Meanwhile, our research shows that 65% of product managers in Poland work with B2B products. In this article, I will present you with five aspects where the measurement of a B2B product differs from that of a B2C product.

For e-Commerce, it is pretty easy to define and measure a sales funnel. A user from a specific source enters the website, finds a product, adds it to the basket, places an order, and makes a payment. We can filter such a funnel after the following:

• the client’s source of origin (organic, paid),
• browser language,
• a version of the viewed application/website (mobile vs. desktop),
• and many others.

Marketing and sales of a B2B product, where sales are very often based on direct contact with the customer, is a much more advanced process to be metered due to the need to:

• obtaining leads,
• arranging meetings,
• implementation of demonstration meetings for clients,
• writing e-mails, and calling the client.

We need to measure all these aspects to be able to answer the questions:

• Is the process of acquiring customers from a specific source effective?
• How much does it cost us to acquire a customer? Are you sure the customer acquisition cost is lower than the customer lifetime value (CLV)?
• Does the client leave with us more than we pay to obtain it

Implementing marketing and sales tools or measuring these processes is usually not the role of product teams. However, we should work with marketing and sales to make sure the process is metered or at least aimed at.

As a product team, we pursue goals related to revenue or conversion, and we have precisely the same goal as the sales and marketing teams. If our value proposition doesn’t work — the seller won’t sell. If our value proposition works, but there are too many mistakes at the sales stage — the purchase will not occur. So we have to monitor all customer experiences with our product.

Suggested solutions

• Arrange calibration meeting with marketing and sales — analysis of the primary metrics needed to make decisions. Verification of the current state — as indicated by the metrics.
• Without measuring key metrics, initiate a tool implementation project or process change to enable data-driven work.
• Implementation of a tool that aggregates key business metrics and presents them transparently to all interested parties

The entire organization must know the product’s target group, which should be distributed among all teams.

It may turn out that, as an organization, we conduct inconsistent activities. E.g., we design a new product for a specific target group, and our marketing campaigns reach a different recipient. We obtain leads from outside this group. It is difficult to convince such customers to buy a solution because a value proposition designed for a different group may not suit them.

Example:

It may be that product analytics will show us the following indicators:

• Cost of acquiring leads — low (theoretically SUPER)
• Conversion from the trial product version to purchase — low (theoretically WRONG).
• Cost of customer acquisition — high (WRONG)

If we will go deeper with analysis and use qualitative research methods (e.g., interviews), We can gather the information that:

• We obtain a lot of leads but of low quality. Low quality means there are leads from outside our target group.
• Is there any other reason for low conversion from trial to purchase?

Suggested solution

• Defining the business model (target groups and value proposition for these groups),
• Conducting research and creating a Value Proposition Canvas, in cooperation with marketing and sales, referring to the created target groups/personas in internal communication.

Measurement of a B2B product is more difficult because we usually deal with single users and many users connected to company accounts.

As product builders, we want to know the answers to the following questions:

• How many clients do we have — that is, how many companies pay us?
• How many users do we have?
• How many users are, on average, assigned to a company account?
• Are our clients involved?
• What does commitment mean to us?
• What % of users assigned to the company should use the tool to determine if the customer is getting value from the tool?

Product analytics should be implemented in such a way as to be able to answer these questions as well. Additionally, we should be able to measure all the primary metrics for our business for specific groups of our clients. So it is good that these groups are marked correctly in the database and integrated with our analytical systems so that filtering is possible.

Suggested solutions:

• Listing all the metrics, we would like to know to make product decisions.
• Including the entire product team in selecting the appropriate product analytics tool.
• Working out an appropriate data architecture with the team will enable us to measure key metrics. Choosing a tool that supports the data architecture selected.

It is not uncommon in the case of B2B products; we sign SLA (Service Level Agreement) agreements with customers about the quality level of the service we guarantee. In this situation, we must be sure that we meet the conditions to which we commit ourselves.

To achieve this, the primary metrics included in the SLA (e.g., availability) should be monitored and reported — preferably available in the primary analytical tool used in the organization.

Suggested solutions:

• Designate a person/team responsible for each class of metrics: e.g., performance, error handling level, etc.
• Agree with the team responsible for specific monitoring and reporting process metrics for these metrics.

In the case of product implementation in the customer’s infrastructure (on-premise), we can most often say goodbye to the possibility of measuring user activity in the product. In this situation, we are only left with qualitative research, such as in-depth interviews, customer advisory boards, or focus research.

Appropriate measurement and monitoring of key events and activities enable the creation of a mechanism for predicting the customer’s resignation from our product. Data can quickly indicate that the customer is not getting value from the solution. However, we must build our prediction considering both company accounts and individual users to achieve this.

In a situation where the person paying for the application is not its user but, for example, the head of the department, you should additionally manage the relationship and meet the expectations of this person.

Suggested solutions

Investigating what factors indicate a high risk of customers leaving and implementing functionality that will inform us or the customer service department about a potential threat.

Organizations must develop a B2B product that all teams work on based on data and that each process element is appropriately metered (marketing, sales, product use, customer service).

B2B customers, especially those who pay the most, can be demanding. Try to listen to their opinions and suggestions for improvements in the product. Remember, however, that actions and short-term projects for one client will not bring you closer to the goals that are strategic for your company.