Customer data is crucial when creating a sales and marketing strategy. But humans are not made of data, so when considering the customer, you must acknowledge that the data itself doesn’t reveal the whole story. How marketers gain actionable insights – and drive growth – through data is a more important question to ask and answer.
In the late 1990s and 2000s, you couldn’t spend more than five minutes in a business meeting without hearing the term “big data.” Then it evolved into “semi-structured data with variety.” Technology has enabled marketers to drink from the proverbial firehose, but the immediate results revealed very little about how customers behave. Further segmenting customer data had its benefits, but much of what was being collected wasn’t actionable.
With the advent of machine learning (ML) and artificial intelligence (AI), marketers can now thread the needle between customer data and actionable insights with greater precision than ever before. Preferences, behavior, engagement, and PII (personal identifiable information) are collected and analyzed as a matter of course. Technology then allows marketers to predict trends, uncover hidden insights, and drive an integrated response to customer dynamics in real time.
Most consumers have seen an online ad precisely targeted to their tastes, aspirations, and needs. Personalization has become so seamless because the customer data that drives advertising is smarter and more predictive when coupled with AI and ML technology. But because people have access to so much information, and there are so many external influences on consumer behavior (most notably social media), harnessing insights from data is an exercise of precision.
We don’t need more data; we need more actionable data. We need to know when a customer needs a particular product or service. We need to know how they fill their consideration sets. We need a way to reach consumers who don’t yet know they need our product or service. And then, we need a way to understand their post-purchase behavior. Will they tell a friend? Will they write a review? Data may help marketers bring customers to the watering hole, but it can also tell us when and how much they drink.
Marketers have many tactical tools for gaining strategic insights from data, but many fall short in trying to make the data useful and actionable. Moreover, they put data and insights into a silo, doing surveys and retrieving mountains of “big data”, but not revealing the right insights or tying them to strategy. You could have all the data in the world, but if it's not used to refine strategy or help prove or disprove hypotheses about how you should be operating, then you run the risk of “your data” not amounting to much and likely have wasted considerable time.
Marketers should be thoughtful and strategic about what they want to learn about customers and the market so that they are asking questions and collecting data that can answer those questions; which in turn can translate into a plan of attack. Don’t simply ask for or collect data for the sake of it; make sure that you're being intentional in what you collect and compile. This will make it easier for you to align the insights gleaned from customer outreach with strategic objectives once the data is in hand.
Gaining insights from data doesn’t need to be complicated; it starts with using the right tools. I love a good survey. I love a good longitudinal study. These don’t need to be complicated. You can use multiple choice and yes-no’s to get a sentiment. Personally, I have found having no more than 10 questions in a survey to be an effective tactic in ensuring respondents provide value information I run a survey and make sure that the questions are clear, and understandable, leading to either proving or disproving or a hypothesis. The questions should also allow space to uncover something statistically significant that you didn't think of. Talking to two people about something may not be significant. But from there, once you get some direction, you can do a group of five people to refine.
Afterwards, I leverage focus groups after collecting data as a means to get people's reactions to a prototype of a concept. I also think it’s helpful to use data to slice segmentation and then create a hypothesis around your segmentation to test and refine. This is where data from a technique perspective can be helpful for A/B testing as a means to refine your insights.
What can transaction data tell us about customers? It can reveal seasonal purchase trends. It can tell you the volume and frequency of purchase and, through that exercise, can help you calculate customer lifetime value (CLV) for each purchaser. It can inform your measurement of the cost of acquisition, too. Once again, these tools give us a base-level proof point for our marketing hypothesis that we’ve developed using big data and segmentation.
This exercise is not a “one-and-done” proposition. Because consumer behavior is not a one-time thing. Marketers must continually refine and test their marketing hypotheses throughout the lifecycle of a product or service, accounting for changing trends, economics, and purchasing behavior.
As marketers, we must be more thoughtful and intentional about the questions that we're asking, recognizing the limitations of collected data collected either directly or indirectly. From there, it’s important to make sure that you have a sense of what you could potentially get. The worst thing you can do is spend
Why is this ineffective? Because marketers aren’t asking the right questions. This goes back to being intentional about the results you seek. Repetitive questions or queries that won’t result in actionable data are simply a waste of time. The best way to avoid this is to have your strategic goals identified before you seek customer feedback. Then, use all your tools – demographic and psychographic segmentation, survey results, customer behavior, and focus groups, to name a few – to pull relevant data into your planning cycle.
Yes, data is big. But we don’t need to boil the ocean. Marketers and their companies will do better focusing on the small details that truly help them leverage insights to benefit the brand, products, and the bottom line.