By Nick Kharas – Data Scientist, PreciseTarget
The post-COVID-19 economy has changed how consumers shop. Retailers are closing stores and going online, or resorting to other desperate approaches just to keep up. Those who couldn’t pivot to digital simply filed for bankruptcy. Do you see any patterns here? How did embracing digital transformation suddenly become a necessity?
The importance of Audience Targeting
In online marketing, knowing whom to reach out to matters just as much as how to reach out to them. Digital advertising channels are plentiful, but finding the right audience is still an unsolved problem.
Some are born to love your product, and some will never care. Most, however, are actually somewhere in between, undecided and needing persuasion. The problem for marketers is that they don’t know who’s who.
Online marketing costs are directly proportional to your campaign’s audience size. You can’t reach out to every single person on the planet who has a cell phone. You also can’t throw darts and randomly select a subset. You want to maximize your conversion rate – the number of people your ad will persuade to buy into your brand and purchase your product. How do you get there?
Going Beyond Demographic Segmentation – the Power of AI
Demographic segmentation has been a favorite audience selection approach. Slicing and dicing data by demographic factors can reveal groups more likely to buy into your brand. This approach, however, relies on a lot of generalizations and guesswork.
Artificial Intelligence treats this problem differently. It doesn’t label your consumers based on their demographic segment. Rather, it attempts to identify their personas. Hundreds of demographic factors related to income, age, geography, and household composition, can be summarized into just two key components:
- Life Stage – A young, unmarried professional renting a tiny apartment in a crowded city can have very different spending habits from someone owning a house in a suburban neighborhood and starting a family, even if both are the same age and earn the same income.
- Personal Priorities – Everybody has different interests in their social and personal lives. Everyone experiences their own unique circumstances. Glancing through demographic data alone can’t tell you the complete story.
Big data analytics also provides the ability to pair these insights with consumers’ past purchase history, giving a holistic view of not only your consumers’ personas but also their tastes. A strong AI platform can rapidly and frequently run computations and generate recommendations in real-time, allowing marketers to focus instead on making sound business decisions.
With a strong AI strategy, you can cut through the noise in demographic factors, and get to know who your consumers really are. You should be deliberate about reaching out to the right audience. If you don’t, you will miss a massive earnings opportunity at best, and waste a lot of money on unfruitful campaigns at worst.
Is Your AI Generating Business Value?
Of course, it is not easy to trust AI if you don’t understand it. Fortunately, data science also provides a framework to monitor the performance of your AI platform, and safeguard you against failures. There are several ways in which you can accomplish this:
Well-designed diagnostics for predictive models reveal:
- What portion of your intended audience is actually not interested in your product?
- Who is the audience you did not reach out to, but would have actually bought your product?
Your data science team must identify and “learn” from these failures, and keep improving your predictions. The AI platform should offer a clear view of not only the marketing dollars wasted but also the missed opportunities along the way. This is the key to successful data monetization.
A/B testing is the safeguard. It is the most scientific, yet intuitive, approach to poke holes in your assumptions and course-correct before it is too late. You may have built highly accurate predictive algorithms. However, do they align with your business objectives? Do they really perform better than traditional audience targeting approaches?
The best practitioners understand that you test, fail, learn and repeat. Fail early and fail fast, so that you have full control over your marketing budget.
An aggressive A/B testing program can validate your AI recommendation. This will save a lot of marketing dollars by validating the results of predicted segments at a much smaller scale, while also giving your data science team a feedback loop to improve AI models. Establishing an AI model is not a set it and forget approach. They must be continually tested and evaluated based on how business objectives change and changes in the market.
When running an A/B test for a marketing campaign, keep in mind:
- Focus on measuring the impact on your conversion rate. This can only be possible if you collect data on how many customers made a purchase through your ad
- If you can’t get the conversion rate, the click-through rate can be a good proxy.
It is amazing to see how many brand owners and retailers are sitting on a treasure chest of data, but not monetizing it. The effects of COVID-19 won’t go away soon. This is the time for digital innovation if you want to distinguish yourself and win the long game.