English writer Dennis Potter once said, “the trouble with words is that they do not always say what they mean.” There are two major concepts affected by this problem. One is “data-driven marketing,” which most people today understand to mean digital marketing based on various algorithms that adjust communications based on the behavior of the target audience. The second problem area relates to what is referred to as “purchase price.”
Actually, all communication should be data-driven – and it often is. At least if we mean having data to base our marketing decisions on, regardless of channel or activity. Most importantly, we should refine those data points into information, insights and knowledge. This means that we marketers should be knowledge-driven – not just data-driven – in at least these four areas:
- To understand the company’s business. Key aspects such as different customer segments, willingness to pay, product mix, pricing, and distribution issues are the foundation for marketing that not only sells more, but more importantly, sells more profitably by getting more people to buy more often and pay a little more.
- To understand human behavior. How do people make decisions? And last, but not least, what characteristics, associations and attributes influence us to prefer Brand A over Brand B while thinking Brand A is the better choice, even if it’s a bit more expensive?
- To understand our customers and prospects. Why do those who buy from us buy from us? And why do those who do not buy from us opt out, and what can we do to attract them? What are their needs, and more importantly, what hidden drivers influence their decisions and willingness to pay?
- To understand how different marketing communications initiatives impact the business. Long-term vs. short-term, emotional vs. rational, broad vs. focused, penetration vs. loyalty, and the importance of the equation ESOV = SOV / SOM are some examples of key issues where there is a lot of empirical and proven knowledge about what makes the best impact. At the same time, there are also many examples of the penalty paradox, i.e., many choose not to do what has the greatest chance of producing results because they prefer to follow the law of least resistance.
Data-driven marketing is based on the premise that we can learn customer behavior and, by targeting different – and tailored – offers to them at different stages of the buying process, convert their interest into a purchase. The more data, the better the customization and the higher the relevance of the offer to the customer. And like a letter in the mail, conversion and increased efficiency then occur. Then it’s also easy to be seduced into believing that data-driven advertising is the solution for marketers today and tomorrow.
Data does not lie, but it also does not tell the whole truth.
There are two fundamental problems with not recognizing the difference between data-driven and knowledge-driven policies. The first challenge is in the analysis. It’s easy to see conversions, and it’s both clear and understandable to see them as a measure of how well marketing is working. But often the analysis overlooks two fundamental components:
1. How many would be purchased even without processing? To maximize efficiency, algorithms focus on those in the target audience who have a high (highest) likelihood of conversion. But if the conversion probability is high, it will be even without processing. So, the risk is that you pay with offers that lower the margin for customers who would still buy from you. It’s a bit like handing out discount coupons at the pizzeria when the customer is already at the register. Besides, many of them are often already your customers – and happy with what you offer – even without discounts and deals.
2. How many of the buyers have actually been processed? That there is a tracking cookie is one thing, that they actually show up is another thing. This article is one of many on the subject, dealing with the dilemma of views and digital statistics.
The dilemma with the idea that precision is the most important – and sometimes only – solution for today’s marketers is that it runs counter to the general knowledge base in the four areas mentioned above. And the main reason for this is probably the mistake of confusing the buying journey with the customer journey. The idea of better precision is based on the fact that the customer primarily wants advertising for something for which he or she has an active need, and that it is only in these situations that they perceive advertising as relevant – because they are in the buying process.
The more you sow, the bigger the harvest.
But then we miss perhaps the most important part of the customer journey: creating positive knowledge before the need arises and before the buying journey begins. There is a wealth of data showing that something as basic as (positive) knowledge is the foundation for both sales and profitability. If you do not engage with the target audience until they have a need, it’s difficult to create space to build a positive perception. Instead, you often end up going straight to discounts and price offers as the most important messages. However, if you have already created positive knowledge during the customer journey, i.e., before the need to buy arises and the purchase price comes into play, then there are good conditions for converting this into profitable sales.
And this is where data-driven marketing – or rather, insight-driven digital marketing – has an important role to play. Data-driven (insight-driven) digital processing is one of the tools in the marketer’s toolbox, but this tool does not replace all others. And most importantly, if you use the other tools properly – and based on the collected knowledge that exists in all four of the above areas – you will maximize both the impact and the effectiveness of your data-driven marketing and other efforts. There is both data and accumulated knowledge that shows.
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