An Analytical Approach to Dealer Sales Conversion

Analytical Advantage is developing a pilot program to help dealers have a better understanding of their customers and use in-depth analytics and variety of data to help-drive higher conversion, higher revenue, and insightful decisions.

This is not software or technology, this is insights and a change of mindset.

The combination of customer-wide data elements with the human intuition and development of predictive models is what drives incremental profits. 
Customer lifetime value
Driver higher conversion with personalize marketing campaigns and finance offerings
Prioritize leads by probability of purchase & estimated net value of deal
Know which customers are in need of a new vehicle & are equity positive
Predict those who will purchase warranty products
Predict customers who will provide long-term value, e.g., maintenance

CREATE

ACTIONABLE VALUE

Let's compare John & Bill's purchases and see how timing is everything

Customer Lifetime Value (CLV)

What is CLV?

The basic definition of customer lifetime value is the expected return of a customer over their lifetime; the fundamental purpose of CLV is to create a unique view of each customer's value and derive a ceiling to customer acquisition spend. The analytical derivation of 'value' is likely to vary across business types and the intended use. A retail business may may have a completely different equation for CLV than a hotel chain. 

Regardless of industry, everyone is looking to gain insights into their customer-base to grow and nurture their business. Below are some other great resources for CLV overview. 

How Can You Use It?

Sure, it's great to have the numbers, but execution is key; to many executives, CLV is just another buzzword or thinks it's too difficult to implement. 

  1. Data mine the most (and least) profitable customers

  2. Knowing the most profitable acquisition channels

  3. Manage your database as a portfolio of wealth

  4. Forecast future revenue based on current customers ​​

  5. Predict a customer's value from the onset

  6. Proactively target risks and opportunities

  7. Personalize marketing campaigns 

  8. Customize rates and terms

  9. Correlate with third-party data to drive insights to margin

View Sample Playbook Execution

 

Pockets of Growth / Decline.

What are Pockets of Growth/Decline?

Pockets of growth can be viewed as an early indicator or an algorithmic way of managing the lowest levels of the business. Most organizations will track and assess the fundamental market segments of their business, for example, Direct, Third-Party, Web, etc..., or by product; however, being able to understand potential risk and opportunities, understanding the trends of the all permutations of your business is crucial. 

That is a lot of data and trends to really look at, and we get it, not enough time or bandwidth to really understand all subsegments of the business. That's why we use computer power and statistics to really narrow down on major segments of the business that are trending up and that are trending down. 

 

Product Offer-up & Anticipation

What is Product Offer-up?

If you are a business that have customers purchasing multiple items, there opportunity to increase revenue through cross-selling, recommendations, promotions, and product placement.

Just like Amazon and Netflix offers you items they think you'll like or items that are commonly purchased, you can have access to that level of insights and algorithms for simplified cost. 

By 'offering-up' recommendations, you can increase your average order value (AOV) and items per ticket. Furthermore, this can be used to optimize your bundling strategy as well as creating great talking points for your call center.

By reverse engineering who are buying what combinations of high frequency set of combined products, we can help you understand who they are and what they do. These results can improve your target-marketing efforts, yield higher conversion rates, and provide valuable insights to value proposition. 

How Does It Work?

One of the most underutilized tools in machine learning is a method called market basket analysis, this is essentially how supermarkets learned to optimize their layouts and learned men who buys diapers generally buy milk and beer with that purchase. 

We aggregate all your customers with all their purchased items on their unique tickets and understand the patterns of which items are often purchased with another items. 

 

Propensity Models

What are propensity models?

Generally speaking, propensity models are probability models that helps identify who among your audience is most susceptible to an event. Or what combination of elements can lead to maintenance failure. 

The outcome of these models are contributors or influential factors that helps define the why and how sensitive certain parameter are to the resulting event. 

The outcome are contributers or influential factors that helps define the why and how sensitive certain parameter are to the resulting event. 

The definition of an event can vary:

  • The event of a customer:

    • stopping payment (probability of default)

    • making a purchase

    • accepting an offer

    • unsubscribing

    • returning​

    • upgrading

    • switching companies

    • yielding low margins

 

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