Very good data. And then what?

So what, or the classic "so what", is the typical question when we talk to eCommerce managers about customer data and predictive analytics.

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What do I do now with all this data? 

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Beyond the size, industries or level of sophistication of the company, we note that in a large percentage of eCommerce stores there are situations with respect to data that need to be paid attention to:
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• More than 65% of month-over-month revenue comes from customers with a SINGLE purchase.
• 80% or more of your customer base is INACTIVE
• The acquisition cost is not identified and, if it is, the customer's Lifetime Value is not clear.
• Your mailing, SMS, push or Whatsapp campaigns have less than 4% conversion rate.

Now comes the SO WHAT: your business, no matter how much it grows, does not work as it should and is not as profitable as it should be. In verticals such as mass consumption, fashion, or beauty, your revenue should be month to month in 60% to 70% of recurring purchases. 

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This implies that month to month only 30 or up to 40% of the business revenue will come from acquired customers. 

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Why do we keep spending on acquisition and advertising without rhyme or reason, if 80% of the customers we acquire are not buying from us? Isn't this one of the main assets and treasures of business?

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Now another fact: retaining a customer costs less than 20% in investment and has a 55% higher conversion rate than acquiring a new customer.

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So, what should we do with all the data?

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Increase Purchase Recurrence: 

Decrease the window between orders and increase the number of orders per customer. Focus on all customers that are approaching the ideal window of repurchase days, analyze their characteristics, send personalized communications to make this window and the repurchase more efficient.

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Reactivate inactive customers:

Of all the thousands of inactive customers I have, how do I segment them according to whom to reactivate?

We can create an audience with the 10% of customers with the highest LTV.

Let's imagine that we have 10,00 inactive customers. 

We will focus on 1000 of them. Of that number, we will take the 500 with the lowest recency (inactive for the shortest time). 

We analyze:

• What they bought
• When
• Which products converted best
• Which product categories
• At what times of the day did they buy
• Which discounts generated the most transactions for them

This type of data-driven campaigns generally achieve a 20% conversion rate. Let's imagine that we have an average ticket of 100 usd, and every month we execute it following these steps, we generate only for that month revenues for 100,000 dollars, from customers that we had in our base and we did nothing with them.

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Identify who my ideal clients are:

What do we mean?

To understand how an ideal customer of my brand is composed: 

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• How many purchase orders?
• With what recency?
• Where do they live?
• Which product do they buy on their first purchase, which on their second and which on their third? 
• What categories move them the most?
• What is your LTV? 
• How do you pay? What payment method do you prefer?

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This allows us to build an ideal customer profile, which with lookalike audiences we can go out and look for more like them and that the LTV/CAC we talked about before, is favorable.

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Convert single-purchase customers to repeat customers:

The first thing we should do is to understand what path other repeat customers took, what purchase path led them to become repeat customers, at what times and what attributes those orders had.

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Then, focus on generating personalized campaigns to get these one-time purchase customers to buy the next ideal product.

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Here, we change from customer to product and analyze which product is an ideal match with the moment or stage of purchase, with the customer's characteristics and allows us to have an ideal offer at the ideal moment.

The belief is always that the way to succeed with sales is via ADS (Meta, Google, Tiktok), when in reality the zero party and first party data of businesses is the biggest sales bank they have after they are established.