In this use case, we want to provide product recommendations to our sales teams to drive sales and incetivize buyers to purchase products at higher margins.
Situation description
For this scenario we will assume the role of a pricing and data science manager at a large global distributor. The present state of the business makes sales teams unable to effectively target customers with upsell or marginal growth recommendations or cross sell, or portfolio growth recommendations. But here comes Pricefx to the rescue giving you the chance to create optimized product recommendation and measure the potential impact before publishing guidance to the sales teams. This results in greater wallet share, increased margin, higher win rates and improved customer satisfaction.
Workflow
Let's see how we can improve margin with optimized product recommendation in Pricefx.
Step 1: Optimization capability
We'll start off in the optimization capabillity within Pricefx. In this case, you'll see there are a number of different out-of-the-box models that we support and these are just a few. But on our demo today we'll focus on product recommendation.
Step 2: Product recommendation - Definitions
LEARN MORE: Product Recommendation is an accelerator. To know more about deployment, use and benefits, click here.
Good to know: Like most optimization modules, there is a strong dependency on quality data. To understand why this matters and how you can make the best of your data, click here.
What product recommendation does, is allow us to recommend products based on a variety of criteria including past purchase behavior, wallet share or propensity to purchase, and other business rules that we have in place. It's a two step process where we define and then review our project recommendations. The first step is taking a look at the transactions and scope.
Based on the data source that we're taking a look at, what this essentially does is allows us to filter down to the parts of the business and products and customers that we want to focus on for this optimization, and filter out those transactions and parts of the business that aren't important.
We can also take a look at recommendation parameters during the definition, which helps us see minimums and maximums around the number of products that we're recommending and the number of transactions that would drive this kind of recommendation.
Step 3: Product recommendations
So let's take a look at step two now within Product recommendations. Within step two, you need to take another couple of sub steps.
Recommendation summary
First, we'll take a look at the recommendations that have been created as we run the optimization. You will be able to see within the dark green actual segments. In light green, you look at customers and in dark and light blue, we'll take a look at primary and secondary products that are recommended. This gives us a nice visualization around the kind of clustering that we might see within our final optimization, but we can drill down into actual recommendation results.
Recommendation results
In this case, we've filtered down to a single customer and we can take a look at the recommended products and the purchase frequency recommended ranks for those, as well as more detail around customer segment information and recommended products.
And segment detail, last but not least, we could also manually add recommendations in the case that the data doesn't really support the strategy that we have moving forward. We can add individual, customer and product specific or segment specific recommendations that we want to have associated to those in addition to the optimized recommendations.
Step 4: Product recommendations in Quoting
Switching gears a little bit, let's take a look at this from a seller's perspective. When we look at a bid or quote within Pricefx, we have multiple individual line items that are associated to the quote. But from a seller's perspective, in addition to optimized guidance, you might want to take a look at optimized product recommendation. What this helps us do, is take a look at individual product recommendations and the reason for that recommendation in terms of things like substitute product, margin improvement or other descriptive categories telling us why the product is recommended and how it should be used in the sales cycle.
This is how you can achieve improved customer satisfaction and profitability by using Pricefx for optimized product recommendation.