Businesses that belong to the B2B space have unique KPIs and reporting needs. This is because of the way they sell, it’s different, which means that the way that they measure is also different. Instead of thinking about visits, engagement and conversions alone, B2B companies also think about prospects, opportunities and win rates. There are multiple ways to customize your current dashboards to not only represent specific B2B needs, but to also to integrate predictive analytics to answer specific questions for this type of business. In this post, we’re going to use data science techniques to visualize the answers to the three different questions below in a Tableau dashboard format:
- Who are your customers?
- Will they respond to you?
- What will they respond to?
Who Are Your Customers?
Before you start tackling prediction tasks, it helps to understand what you’re already working with. Who are your current customers? What characteristics are common to them? Are there certain segments that look and act similarly who should have common marketing and outreach strategies?
A descriptive data mining task will allow you to understand what your current customer base looks like. In this case, we’re referring to a cluster analysis. This will allow you to see visually how different segments of customers exist within your CRM. Clustering is incredibly flexible, and you can cluster customers according to whatever makes sense for your business. This could include:
- Company characteristics: industry, size
- Individual characteristics: title / function
- Website analytics: content consumption, frequency or recency of site visits
- Lead gen data: lead score, name of sales contact, pipeline stage, time in stage, number of opportunities, total win value, win rate, lifetime value
Once you put it in dashboard form, you’ll be able to see who the major segments of your customer base are and how this is changing over time.
This is a question you can answer in Tableau. With the release of Tableau 10 earlier this year, cluster analysis is now an in-product feature. Underneath the hood, Tableau is using a k-means clustering algorithm to find commonalities and groupings in your data, based on their similarity across whichever features are most useful to you.
Will They Respond to Your Campaigns?
Once you’ve got a better understanding of who your customers are, you can start thinking about how your marketing tactics are reaching and engaging them.
The idea of “response” can be defined very broadly here. For a site with online e-commerce functionality, customer response is usually defined as a purchase. But for B2B sites who typically have a longer sales cycle, the response could be a softer conversion point: did they reach out to sales? Complete an inquiry form? Download a product data sheet? Advance down the sales pipeline? The response outcome selected here should align to the realities of your customer’s buying process.
To put this analysis into dashboard form, you’ll need to build a response model in R that predicts the likelihood of each individual customer to respond to a campaign outreach effort. The Tableau – R integration is the way you get the customer level analysis out of R and into your dashboard efforts. For reporting purposes, it doesn’t make sense to report on individuals, so a rolled up and average of the findings across major customer segments and demographics would be of use.
Once you’ve got a calculated response rate, you can also start projecting the ROAS (return on advertising spending) of our outreach efforts or even determine our break-even point with some basic cost information. Tableau let’s you build in dynamic functionality so that you can conduct your own what-if analysis to see the impact of a changing ad cost on ROAS.
What Will They Respond To?
Digging in even deeper, you could also take a look at what creative elements your customers are likely to respond to. This is a more challenging analysis to build, mainly due to the fact that the required data isn’t always easy to get a hold of. There are several ways you could build out this dataset, either through carefully segmented A/B tests, or through careful tracking of historical response rate information for individual customers.
The dataset for this requires as much information as possible about the ad creative: what was the creative like? The calls to action? Did it have images? What was the messaging in the text? Were there other incentives in the creative? Once you’ve built out a robust data set, the analysis occurs the same way as before; by using the R integration in Tableau to build out the likelihood to respond to creative and then feeding that back to Tableau for visualization and dashboarding purposes.
If you put all of these components together: an understanding of your customer base, their likelihood to respond to outreach efforts, and details on what exactly they respond to, you’ll be well on your way to be able to optimize your marketing time and efforts. One advantage of putting predictive analytics into a dashboard format is that it allows you to scale easily across brands, geographies or products. The data set would change for each different dashboard, but the visualizations and R code fueling the analysis could typically remain unchanged.
If you’d like to find out more about predictive analytics techniques along with other advanced industry leading data science applications, check out our upcoming webinar with the AMA: “Applying Data Science Methods for Marketing Lift”, on November 3rd, 2016 at 12- 1pm PST. You can register for free here.
