Retail & Supply Chain

Three steps to harnessing AI for targeted customer acquisition

by AI Business
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by Vikrant Pathak, myautoIQ
24 February 2020

The ways many companies approach customer acquisition today are quickly becoming obsolete. The traditional channels – be it newspaper advertising, billboards, or TV – may work for brand-building, but do little for direct customer acquisition.

Since the 2000s, companies have been investing in digital channels for customer acquisition. However, in most cases, the process is still reactive – digital channels help find only the customers who have already expressed their purchase intent.

In the dynamic era of the growing global marketplace, these strategies are not enough. Marketing is also becoming more expensive and customer acquisition costs have risen by 50% just over the last five years.

Customer acquisition strategies themselves aren’t enough unless they are targeted and proactive. That’s where artificial intelligence (AI) comes in: the technology can help predict consumer behavior, provide real-time recommendations, and continuously improve upon these through powerful machine-learning capabilities. AI can find the right customer for the right product at the right time and reach out with the right message.

Wondering how to reach the promised land?  Follow these three steps to reap the benefits of AI and switch your customer acquisition strategy around.

Step 1: Bring in data to predict consumer behavior

Did you know that 99.5% of collected data never gets used or analyzed? That’s an awful statistic, considering that data is the new oil.

To harness the value of information, analyze your historical data to see what types of people purchased from you in the past. This will build the personas of your potential customers for specific products and categories.

However, most of the data companies have on a customer is limited.  That's why you should focus on building a big data universe that is powered by external data points, including demographics, lifestyle, major life events, and purchases, like house and car ownership, and personal preferences.

After you stitch this information with your historical data, you need to clean the data, identify the key attributes, and remove any noise from the key attributes needed for your customer-product profile using machine learning and statistical models.

For example, your big data may have 500 attributes for a customer – however, only 20 of them may be relevant as decision-making factors of your customers. The rest is just noise.

Once that you have the key attributes in place, you need to identify the trigger points. For targeted customer acquisition, it’s important to reach customers matching the persona when they are ready to buy. After all, if someone fits your persona, but they bought a car today, that would be a wasted marketing effort – but if they’re looking to buy within the next six months, then it’s a good fit.

You may need to look into lifestyle trigger points. For example, if you’re a home fitness equipment provider, one of the triggers may be when someone buys a house. Effectively, what you’re trying to do is predict the trigger points and, ultimately, the behavior of a person based on the big data.

Step 2: Prioritize and personalize engagement

Once you pair your target persona with the real people in your geographical location, you come up with a list of potential buyers. You should focus on scoring and prioritizing these by training your machine learning models on the triggers and attributes to create your target most effectively.

When you have your top leads ready, it’s time to determine engagement triggers and reach out with a personalized message. How do you engage the customer? What makes them tick? And how do you personalize the messaging? Asking yourself these questions is a fundamental part of the targeted customer acquisition process.

For example, if a car manufacturer decides to give $2,000 worth of offers on its cars, for a customer that is more value-focused, the messaging can include discounting that from the original price. For a second customer that is more reliability-focused, the offer could amount to 4-year prepaid maintenance, while for a third one, who is luxury-focused, this could be $2,000 worth of luxurious upgrades.

It’s also important to decide which channels are right for engaging the customer. Experiment with channels to see which combinations and sequences give you the best results. You may even find that for high-value purchases, postal – yes, the channel long thought to be extinct – can also be very successful.

Step 3: Optimize and oversee

But your journey doesn’t end when you reach out to the prospective buyers. The next important step is to ensure that you learn from the results and fine-tune your machine learning models. For example, if you reach out to 100 and 30 respond, you can determine which predictions were accurate and which weren’t – feed it to the machines and make the next prediction cycle smarter. Also, remember to constantly reshape your target persona as you go.

To leverage the most effective algorithms, you can’t just set up the automation and use it as a magic box to carry out targeted customer acquisition for you. So don’t just sit back – actively supervise the machine, optimize, and monitor to prevent any potential bias.

According to DataRobot, 93% of organizations say they will invest more in AI bias prevention initiatives in the upcoming year. If you want to advance on this front, set up alerts for any irregular situations, deploy algorithms to detect hidden biases, and promote AI explainability.

To match your customer acquisition with the ever-growing demands of the new decade, you need to ensure that you reach out to the right customer, for the right product, at the right time and with the right offer. By using AI, you can have complete control over this process – and continuously improve upon it.


Vikrant Pathak is the CEO and Chief Data Scientist of myautoIQ, an American startup that develops AI-powered software tools for car dealerships

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