Retail
AI Solution · Revenue Radar for RetailRetail

Acquire and prioritize the customers who will be worth the most

Revenue Radar builds proprietary predictive models to anticipate the lifetime value of every shopper and captures new high-value leads through automated social media campaigns.

Lifetime value
value prediction per customer
Automated
acquisition campaigns on social media
2-4 wks
implementation
The challenge · Why acquisition budget is spent blind

Not every customer is worth the same, yet they are all chased the same way

Without a model of future value, the marketing team spends the same budget on a one-off buyer as on a customer who will keep coming back to the store or the online shop.

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Generic acquisition

Social media campaigns target the general public, without telling apart which profile will generate more value in the long run.

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Unknown future value

The team cannot tell which customer will buy once and which will become a recurring, high-ticket customer.

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Misallocated budget

Without a lifetime value prediction, the acquisition budget is split evenly between high- and low-potential leads.

The Solution

Value prediction and acquisition in a single solution

It combines a proprietary customer value model with automated campaigns that look for that same profile on social media.

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Proprietary lifetime value model

Builds a predictive model trained on in-store and online purchase history to anticipate the future value of each customer.

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Automated social media campaigns

Launches and tunes campaigns on Meta, TikTok or Instagram aimed at the highest predicted value profile.

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Lead prioritization

Ranks incoming leads by their estimated lifetime value so the sales team knows which to follow up first.

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Continuous optimization

Retrains the model with every new purchase to sharpen the prediction and the campaign targeting.

How It Works · The value, step by step

From historical data to a campaign that captures value

The solution learns from your customer history and uses it to steer acquisition on social media.

Customer historyTrain lifetime value modelDefine value profileLaunch social campaignIncoming leadsPrioritize and hand to sales
1
It analyses your customer historyIt gathers purchase, repeat-rate and average ticket data from store and online to understand what defines a high-value customer.
2
It trains the lifetime value modelIt builds a proprietary predictive model that estimates the future value of each customer profile.
3
It defines the high-value profileIt turns the model into an actionable audience profile for acquisition campaigns.
4
It launches automated social media campaignsIt activates and adjusts social media campaigns aimed at that profile, without constant manual work.
5
It prioritizes incoming leadsIt scores every lead by estimated lifetime value and passes it to the sales team with that priority.
Integration

It connects to your customer data and your campaign platforms

It does not replace your CRM or your ad platforms: it connects to them and adds the predictive layer they lack.

Input

CRM, sales and social media

CRM, in-store and e-commerce purchase history and ad platforms such as Meta, TikTok or Instagram.

Grovium Solution

Revenue Radar

Predicts each customer's lifetime value and points automated campaigns at the highest-value profile.

Output

Prioritized leads and live campaigns

Sends value-scored leads to the CRM and automatically adjusts running campaigns.

The marketing team validates the target profile and the budget; the solution executes and prioritizes.

Use Cases · The Business Value

Where the impact is most noticeable

E-commerce and multichannel retail

Prioritizes the leads most likely to become recurring, high-ticket customers.

Fashion and lifestyle retail

Focuses social media acquisition on the shopper profile with the highest estimated lifetime value.

Loyalty programs

Shifts campaign budget towards the audience segment with the best lifetime value.

Problem → Solution

A retail chain that spent the same budget on every lead, whatever it was worth

A real example of the kind of situation this solution solves.

Before · the problem

The marketing team spent the same budget per lead across all its social media campaigns, without telling apart which profile would buy once and which would become a recurring customer.

Acquisition cost was identical for a high-value customer and for a one-off buyer, diluting the real return of every campaign.

After · with the solution

Revenue Radar built a lifetime value model from the customer history and redirected the campaign budget towards the highest predicted value profile.

Social media campaigns started bringing in a higher share of leads that matched the recurring customer profile, and sales followed them up by estimated lifetime value.

Win the customers who will be worth the most

We will show you, with your own data, how Revenue Radar predicts lifetime value and steers your acquisition campaigns.