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Repurchase analytics – a data-driven sales approach

Use case for how to become a data company,

Is your sales team still manually gathering and processing customer data? Dramatically boost sales efficiency with an AI-based prediction model.


Ask any sales representative what they dread most, and chances are high that ‘cold calling’ tops their list. No wonder: trying to (re)sell a product or service out of the blue means facing plenty of immediate rejection. With a data-driven sales approach, you can dramatically increase the success rate of your sales calls. Instead of relying on gut feeling, use data insights to rank prospects by likelihood to buy – and focus on only the most promising potential customers. Here’s how it works.

Using data to drive sales efficiency

For many B2C companies that sell valuable products and have large customer databases, plenty of potential is lost because there is no singular way to estimate the probability of a sale. Sales teams follow their gut feelings and base their actions on business logic, experience and manually gathered information. This may have worked in previous, slower-paced eras, but in the digital age, it poses some hefty challenges.

Hard to renew contracts
A customer database can be divided into two groups: those who already have contracts – say, a fleet contract with a car dealership – and those without contracts. The first group is easier to renew, and can be effectively called at the end of their term. The second group, however, is much harder to renew, but still holds significant sales potential. So, how do you prioritise the list of clients to contact?

Manual approach
Without a customer data strategy, a sales team will base their approach on their experience and business logic – their instincts, if you will. This implies that the sales team has some experience, of course. But that begs the question: what’s the best way to efficiently onboard inexperienced sales personnel and quickly get them up to speed?

Even more, customer data that is available is often siloed and/or manually gathered. It is spread over different software systems, which means the sales team has to invest a lot of time and effort into gathering information before calling a client.

No uniform lead generation
While an unstructured approach like this may work for some savvy sales reps in certain sectors, it poses structural problems on a large scale. For instance: how do you know if the sales team is contacting the right customers? Are they doing so in the most efficient order? And finally, if every sales person in the team is applying their own approach, how do you objectively evaluate their performance?

In this digital age - to accurately estimate probability of a sale is key to give your company a competitive edge

A prediction model for data-driven sales

Predict how likely a client is to renew
That ‘gut feeling’ your experienced sales representatives have; It’s actually based on a number of parameters, including previous sales and customer information.

Example: Car dealership

Let’s say that a car dealership has information on an existing client. Based on the age of the client’s current car model and the yearly mileage, a salesperson can estimate how likely a client is to buy (or lease) a new car.

What if you could use a prediction model to calculate their likelihood of (re)purchasing based on data that you have about your past sales activities and your customers?

One common misunderstanding is that you need a ‘big data approach’ to do this. In fact, CRM and ERP data are sufficient to build a prediction model, which is achieved in three steps:

  1. Train the model based on sales information from the previous years;
  2. Validate the model based on sales from the last year;
  3. Make predictions for the current or upcoming year.

Naturally, the model will become more and more accurate over time, since the predictions will be validated in turn by actual sales data.

Create a ranking based on likelihood
Once the prediction model is validated, it creates a ranking of all prospects based on their likelihood to repurchase. Of course, the prospects at the top of the list are the ones to call first, since the success rate versus time spent on the sale is much higher.

A cutoff point is then defined. Below this point, the prospect is not likely enough to repurchase to justify the time and effort needed to contact them.

Higher conversion rates, happier sales team

Higher conversion rate
In one car dealership that implemented repurchase analytics, the conversion rate rose significantly. The data-driven approach almost doubled the number of conversions with an increase in conversion rate by 80%.

The data-driven approach almost doubled the number of conversions with an increase in conversion rate by 80%

Integrated into the existing IT landscape
There’s no need to invest in yet another platform to use the prospect-ranking feature. This prediction model is seamlessly integrated into existing platforms like Salesforce, and the call list is presented in reporting tools like Power BI or Qlik. Sales managers can simply filter the list, log results in a CRM system and the reporting tool is automatically updated.

User-friendly 360 customer view
The prediction model offers two views. The call list presents basic data (like contact information), a second view offers more in-depth information, such as past sales. The sales team or manager can easily filter this information and get a 360 view of any customer, no manual labor required.

Objective evaluation
Repurchase analytics offers benefits to the management team as well. Since prospects are quantified by likelihood to renew, the performance of a sales representative can be evaluated much more objectively.

Repurchase analytics: Future possibilities

The prediction model not only offers higher conversion rates, but forms the foundation of a more data-driven approach in general. A unified and centralized customer data platform forms the basis of this prediction model, breaking down silos. If a client logs in on the website, they can be identified immediately, offering valuable opportunities for personalized customer experiences.

Sales and marketing teams can also leverage this customer data for their campaigns, and create a personalized offering via the website, mailings and ads.

Cegeka has a strong foundation in ERP and CRM systems, which enables us to offer end-to-end solutions to support your data-driven transformation. We infuse business processes with AI and integrate this model into existing tools using a proven Cegeka reference architecture.

Cloud Data Platform with Cegeka Reference Architecture


Challenges

  • Accurately estimate probability of a sale
  • Define a customer data strategy
  • Efficiently onboard and evaluate sales personnel
  • Unify lead generation

Approach

  • Create a prediction model to calculate the likelihood of (re)purchase
  • Leverage CRM and ERP data to increase sales efficiency
  • Rank prospects based on their likelihood to repurchase

Solutions


Results

  • Increase in conversion rate by 80%
  • Integrated into the existing IT landscape
  • User-friendly 360 customer view
  • Objective evaluation

Checklist data journey

Where do you stand on your data journey?

Be sure to download our checklist to figure out where you stand on your data journey to become a data company.

Download checklist now

Perhaps we’ve inspired you to work out your own use case?

  • Jan Kesters - Sales Manager at Cegeka

    Jan Kesters
    Sales Manager

Then we will discover together how we can help you to become a data company.