Can You do Market Research for Free? Yes, With Your Own Internal Databases!
Commonly overlooked but invaluable sources of customer data
Good market research is essential to developing and executing great marketing strategies. At OpenView Labs, our Research and Analytics team assists the portfolio companies with major marketing research engagements to help them identify and prioritize segments in their markets, develop and test competitive advantage statements for their marketing, and select the top market influencers for their influencer marketing campaigns. Those projects typically involve a great deal of data collection through either surveys and/or interviews with market participants and are thus quite time and resource intensive. Thus, while very desirable, such projects are not always suitable for a company that is looking to research their customers but do not have the resources to support the external data collection efforts.
However, we often overlook the host of internally available data on customers and their behaviors that a company naturally collects in its course of business everyday. As we shall see, these data sources are sufficient, in many cases, as the sole inputs for other types of market research projects, and can also provide extremely useful preliminary inputs to a larger project, especially in helping guide and narrow the scope of the primary research efforts.
Whenever we start a market or customer research project, we look for the following common internal operational data sources and try use them if they are available before looking for external data sources:
1. CRM Data
This is the data that is collected by the sales (new sales and account management) and marketing team throughout the sales cycle from demand generation to prospecting, opportunity management to closing as well as post-sales activity such as onboarding and renewal management. This data is often stored in a database where each customer or prospect is a record, to which data such as firmographics information, contacts, needs, use cases, interaction histories, pass and current opportunities, prior and current contracts is added overtime. Because of the customer-centric nature of the data, this database lends itself excellently to customer-level analyses. However, CRM database(s) are also notoriously messy. There are always issues with duplicates, inconsistent formats, missing data points, that deter the use of CRM Data, especially by non-sales and marketing team members who are unfamiliar with the quirky ins and outs of the database.
2. Customer Support Database
Most companies have a support-ticketing system, sometimes together with self-service support forum and knowledgebase. Some integrate this into the CRM database (when the underlying platform is flexible enough, such as Salesforce.com). The basic unit of data here is a support case or ticket, together with customer information and all support-related interaction with the customer. This database lends itself often well to analyses of customer use cases, product features and usability issues.
3. Web Traffic Data
Web Analytics tools are not for just the Web marketing folks. They are not even just for the marketing folks. With the increasing sophistication of Web analytics tools, Web traffic data reveals a lot more about the behavior, needs and characteristics of two important segments of the market: people who are interested in learning about your company and the existing customers who go to the website to login to the product, or to find support information. It would be even better if the website analytics is hooked seamlessly into the in-application analytics (for web-based, hosted applications),
4. Customer Billing/Invoicing Information
The invoice and billing records are relatively “boring” data pieces that when coupled with other data sources, paint a comprehensive financial/economic narrative of the customer’s lifecycle, and are essential in analyzing customer lifetime value, and thus the profitability of the company’s economic model.
5. Customer Renewal/Cancellation Information
In subscription or SaaS companies, customer retention is so important that every time a customer fails to renew or extend, the account manager or customer support representative tries hard to capture data on the customer’s reason to cancel. It can be notes on the conversation with the customer on the cancellation, or the text of the cancellation emails. Some companies go even further in trying to standardize these information by categorizing the reasons into a pre-defined set. But equally important is data/notes on how and why customers renew or expand their usage. Unfortunately, this does not get as much attention as the cancellation data, and both sets of data are often overlooked or only superficially analyzed in research projects.
6. Customer Usage Data
This is information often stored very deeply in the IT infrastructure that support the product itself. Usage can be as simple as the number of users, the average number of concurrent users, the amount of data stored, the amount of documents created, and so on. The data, when analyzed per customer, reveal the other side of the customer’s financial/economic narrative: the actual use of the product, and by proxy, the actual value the customer gain from the product. Too often, this data is overlooked because it is hard to get and hard to interpret. One common issue is the mismatch between customer ids in the production system and that of the CRM system.
7. Marketing Automation Performance Data
Separate from both Web analytics and CRM data, a new types of datasets have arisen in recent years, mirroring the growth of sophisticated, massive marketing automation tools, from best of breeds such as ExactTarget (email marketing), HootSuite (Social CRM), to comprehensive suites (Marketo, Eloqua). These tools are structured quite similarly to CRM databases and thus can be used in very similar manner. But because they are also often process-driven, analysis of processes such as conversion rates are typically done.
8. Prior Customer Surveys and Research Data
Last but not least are overlooked data from previously executed research projects. The main reasons they get overlooked are because a.) They did not always originate from the department that is working on the new project, b.) They were not done for the same purpose as the current projects or c.) Turnover at the staff levels cause older, less commonly accessed data files to be misplaced or considered “undecipherable”. All of these reasons should not deter the use of older data. Even though the data might have been collected for another purpose, they could still provide a great starting point for anyone looking to understand the customer or the market better.
So what types of market research projects can be done with these internal data sources? We are going to list here the few that are most common:
1. Web Referral Analysis: By tracing back the Web referrals of visits to the website (using Website Traffic Data), we will find the main sites/sources that are sending visitors to the website, and find ways to optimize them to get even more traffic (of the right type).
2. Lead Source Analysis: Taking the Referral Analysis further, by cross referencing the Website Traffic Data with Marketing Automation and CRM databases, we can develop an all encompassing understanding of the main sources of leads (direct, referrals, and paid) and their relative performance.
3. Lead Prioritization Analysis: Having analyzed the Lead Sources, we analyze the leads further into the prospecting cycles by identifying the leads that go the furthest into the prospecting cycles, and then develop a predictive model based on known lead’s properties to prioritize them, so that we can prioritize leads that historically have the highest chance to convert to the next stage.
4. Website Conversion Analysis: Go even further into the sales cycle, we can combine Web Traffic Data and CRM Data to analyze the conversions that happen at various stages in the sales cycles, from conversion from visitor to a lead, from a lead to a webinar attendee, or to a trial user, and from trial user to paying customer. The basic principle of conversion analysis is to see the ratio of the inflows and outflows of the conversion point and find factors that impact that ratio. Each of these conversion points can be optimized using design, copywriting and user experience techniques that potentially improve conversion rates significantly.
5. Sales Win/Loss Analysis: Another type of conversion analysis can be done with the same principles, but on opportunity history data, which lives in the CRM. This is the Win/Loss analysis for each of the stages of the opportunity life cycle.
6. Customer Growth (Customer Lifetime Value): This classic analysis, favorite of many VCs and Retailers, can be done using the Customer Billing Information, or, in conjunction with Customer Usage Data, describe how customer grow or churn away over time (in terms of both revenue and usage). The Customer Usage/Billing growth analysis will also reveal pricing dysfunctions (for example, when some group of customers’ are paying too much or too little because of their particular usage pattern) and opportunities to optimize the onboarding process. Further slicing the data into time-based or segment-based cohorts then provide powerful insights into how the business has evolved over time (for example, with successively larger groups of customers that uniformly grow faster).
The above are just some of the exciting research analyses that we can execute on internal databases to help optimize sales and marketing operations. Best of all, these databases are yours to use and analyze at any time!