Optimize Retail Marketing ROI using Bayes Analytic

The Bayes Analytic engine provides a phenomenal ability to select customers that have a high probability of responding favorably to a given offer.     This kind of tuned campaign delivery would allow you to use the same marketing investment but run many more campaigns each of which is delivering additional revenue.     From the customer perspective you are presenting better  offers with products and services they actually want to buy so they pay more attention to your future communications.

Bayes Analytic is interested in partnering with leading consulting firms on behalf of their major customers who need to improve the number of closes per customer contact.

In retail marketing it would be similar to taking a marketing campaign that was previously sent to  a million customers with a 3% or 30,0000 people success rate and pre-selecting the 25,000 customers most likely to respond favorably.    This allows you to reduce  marketing investment for the campaign by  39 times while retaining a majority of the  favorable responses.    This technology can multiply marketing ROI while increasing revenue.     The engine has commonly demonstrated the ability to group 85% of the successes into less than 15% of the prospects.   It may not always exceed 85% but even eliminating the lowest 50% probability prospects could cut the marketing spend by 1/2 while retaining a majority of the revenue.

The Bayes Analytic engine is composed of  several different kinds of AI based analytic engines built from the ground up to analyze gigabytes of information in near real-time.   The ultimate goal is to make purchase recommendations based on most probable success.   The same core engine can be applied in retail cross sell, up-sell and marketing.   This system has recently been able to demonstrate phenomenal success at selecting offers based on statistical success criteria.

We can normally harvest the data we need from your existing CRM (Customer Relationship Management),  POS (Point of Sale),  E Commerce and other existing or legacy systems.  We can also generate the output so the can feed back into existing sales and marketing tools.    Part of the process I provide is the analysis and discovery of where your data is located as well as providing the plan and or the software needed to convert it into forms required for use in high end analytic systems.     One of my specialty skills is a strong ability to find fast and cost effective ways to integrate data from many disparate and normally incompatible sources so they can be used together in the analysis process.

Also great to optimize performance of inside and outside sales people

For Direct sales the numbers are smaller but the ROI per hour of sales  effort may be even higher.     If you had a total of 1000 customers of which about 30 are likely to respond favorably to a given offer.   This software has the ability to select 25 of the 30 highest probability winners so you can focus on calling them first.   It can also rank order the rest so you can focus your time and energy where it will yield the highest ROI.

The critical requirement is for the company to have a durable relationship with the customer so they have or can build a history of customers past actions.    It is even better if they have a history of what offers were made to specific customers and how the customer responded to each offer.  If they have this data my system can be adapted.  If they do not have the data then that is the first thing I would have to build for them.       Companies with strong outbound marketing and durable long term customer relationship programs where they also have purchase history would be the best candidates.    Costco,  ATT Wireless, Comcast and Tiger Direct are examples of viable candidates since they have all of these characteristics.

Find relationships humans never would

Sometimes the engine can find relationships in the data a human would miss due to the noise and volume.      For example,  I can now tell you that certain stocks are 3 times more likely to make money if purchased on a Monday while Thursday purchases commonly lose money. I The engine also found that  some stocks have a 75% chance of loosing money if purchased during the 3rd week of the month while also showing that the highest probability wins for many options are those purchased between 13:00 and 13:30 on a Monday.    You can correlate this to customers and find that some customers will respond to offers for housewares well if presented on Saturday but you will experience a 90% higher failure if the same offer was made to the same customers on Monday.

Premium Consulting Partners may use the Bayes Analytic engine for their Customers which may facilitate establishing a new line of  Business

I prefer to approach consulting work from the perspective of bringing a unique value which provides a distinct competitive advantage for my partners.  I normally work with high end consulting companies who have a desire to create new high value projects with their premium customers.  With that in mind I am willing to make the Bayes Analytic engine available for use in projects which purchase our consulting services for the initial implementation.    We have limited resources so the consulting partner will be able to sell a number of additional consultants when implementing for major customers.

In particular we need an anchor client who has a durable relationship with customers,  has the ability to track the success or failure of offers made to customers,  who has  a direct marketing effort and who would be interested in improving per campaign ROI while also improving their customers perception of value received.     This project is likely to need more than just my consulting so your upside would be supplying the rest of the team.   There is a possibility of leveraging it into profitable line of practice using a  solid win from the initial client as a reference.

Joe Ellsworth
CTO of Bayes Analytic

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