Knowing who to spend Money on

The 80/20 rule is an uncanny predictor of so many organizational trends. Almost without fail, you will find that 80% of your revenue comes from 20% of your customers. However, when most organizations prepare to spend massive Sales and Marketing budgets on their market outreach, they rarely ever exploit efficient methods of growth and retention. Rather they focus on gut feel instinct planning and rearview reporting to guide their decisions. This usually results in a lost opportunity to convert loyal customers or reach out to your most probable up-sales. There’s only so much money to spend chasing ghosts, so don’t.

To build more predictable pipelines and sales you need to be targeting the return buyers that have a probability of becoming loyal. These are the prospects that deserve your marketing, sales, and support dollars. And it turns out that not every one of your loyal shoppers should get your sales, marketing, and support dollars. Your targets should be the customers that have not developed the loyalty habits as of yet, but show the signs that habitual loyalty is in their DNA. This is where we begin to see real targeted spending for marketing; where the coupon or incentive program really makes sense.

In this whitepaper we will be outlining how the Intricity Data Science as a Service offering can help you deliver effective Customer Scoring in a painless cloud based data interchange.

 

The Intricity Data Science Team

Intricity’s has been working in the data discovery space since its inception in 2005. Since then, Intricity has developed Enterprise wide solutions for Data Integration, Business Intelligence, and Data Discovery. On a regular basis, Intricity has found organizations that are only skimming the surface of their potential discoveries from their data. This gap was something that Intricity’s Data Science team has been addressing for the last decade. One common organizational oversight is deep customer analytics. Rather than really understand customer trends, most companies are still “driving with the rearview mirror” using descriptive customer behaviors like “sales history”. https://upload.wikimedia.org/wikipedia/commons/c/c6/Victor-Mousetrap.jpgWhile this information is certainly useful, and an important stepping point, it’s not predictive in nature. To get to predictive customer behaviors, organizations must take a scientific stance to the trends and triggers that are already available in their source data.

The Intricity Data Science team originally developed solutions to this problem through individual consulting engagements. During these engagements, Intricity developed an algorithmic foundation of human behavior related to customer loyalty which rendered above 90% accuracy in predicting return customer behavior. However, the real breakthrough was the later discovery in being able to predict that loyalty down to the individual customer level. This was a departure from what most prediction models did by lumping segments together.

In addition to customer loyalty, the Intricity Data Science team is being leveraged as a Data Science arm for many organizations. In a Data Science as a Service approach, experiments can be conducted then delivered as regular refreshed predictions which the organization can run on Intricity servers. We literally can become your Data Science team.

How Customer Loyalty as a Service Works

When you become a client of Intricity’s Customer Scoring as a Service (CSaaS), you will be given a secured location to upload your customer transaction data. The richer this data set is, the more flexability we will have to provide deeper insights. But at the very least we need a unique customer Identifier and the dates of their purchases. Additionally, the longer the purchase history we gather, the more we will be able to train the model which will make the predictions more accurate.

When Intricity runs the CSaaS against your data, we split the data up into two segments. The first segment is the training segment and the second segment is the proof segment. So imagine we received both 2015 and 2016 data. We would use the transactions in 2015 to train the model and the transactions in 2016 to validate whether the model accurately predicted 2016 customer behavior. When Intricity delivers predictions against your data set we also deliver to you a test confidence score.

From here Intricity’s trained model moves to predicting future periods of your choosing. For example, If you wanted to see a prediction of the next 12 weeks of transactions, the model will churn out at a list of customers and how many times they will buy from you in the next 12 weeks! This is the ultimate Sales & Marketing list. Additionally, we score the probability of customers becoming loyal, including what we would call “dead customers”. These are people that have a near 0% probability of shopping with you again. This is also signifigant as you can avoid spending your budget on throw away efforts to people that won’t shop with you again.

This information acts as the foundation which drives predictions into a vast array of other perspectives. For example, by combining the probability of purchase with the location demographics you could determine that marketing efforts are better allocated to s specific region over the coming months. Or imagine you are planning a sales campaign, and you need to prioritize your outreach so that reps aren’t dwelling on customers that have a high likelyhood of buying. Rather their sales efforts go to the fence sitters that have a strong probability of becoming loyal but are not there yet.

 

How Do I Analyze the Loyalty Data?

Intricity allows you to either download the results aditionally Intricity produces a briefing book which provides a dynamic perspectus on your customer loyalty and the patterns that Intricity is seeing in your data. Addiontally, when you have Intricity as a Data Science partner, we can conduct the integration of your other data attributes as a service to deliver analytics in combination with the Data Science results.

As data is refreshed and sent to Intricity, we will in turn refresh the results enabling you to keep up with the latest trends in your customer loyalty.

 

How Do I Get Started?

Go to http://www.intricity.com/customer-data-science and we will send you a token for your own login to upload your data.

 

How Much Does it Cost?

Prices start as low as 3,000 dollars per month. We can give you a free trial run to test the accuracy and predict the next 4 weeks!

If you have additional questions you can book a time to talk with an Intricity Specialist at: http://www.intricity.com/intricity101/

 

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