Being successful in sales is not just about having the right product or service. For every customer who is a perfect fit for everything you provide, there may be many more who are only interested in some of your products.
To sell more effectively, the key is to understand what those customers or prospects perceive as valuable and focus your selling efforts there.
Imagine that you have a prospect where the relationship is still new and you want to close a significant amount of business with the company. How do you go about setting the price guidelines for any deal? This is where segmentation comes into play. While many marketing, pricing and sales professionals know about segmentation, many are not clear how to take advantage of this approach.
Good segmentation is not just about splitting customers into regional or industry groups. Rather, it’s about evaluating in more detail what customers really have in common. Furthermore, there may not be just one segment strategy. Segmentations can take various forms and sizes depending on the questions you want to ask.
So how do you know the right questions to ask? A lot of the information that can help in your segmentation strategy is already available from your own data.
It’s here that data science comes into play. Every customer interaction – from initial discussions and proposals to sales transactions and purchase order history – can be used to predict how those customers will respond to future sales offers. This information can be used with other organisations too, based on their own purchase histories and backgrounds.
By using data science and looking at offers similar companies responded to, it’s then possible to gain more insight into which parameters to use. This includes providing sales teams with guidance for negotiations and pricing levels and showing them the target ranges and their customers’ willingness to pay. Customers that fall into different segments will have different pricing guidance, based on estimates of what the customers in each segment perceive as valuable and what they want to buy.
Let’s look at an example. Here in the UK, one logistics company used a traditional approach to pricing with different price lists for their customers in the north of the country to those in the south. One of these included prices that were higher to cover the additional costs of doing business in the region and to maximise potential profit. This itself is a very simple segmentation strategy. However, this approach was not working for the sales team because they saw customers put off by the higher prices. Their approach led to more discounting to retain business, while valuable services for higher-value customers were often included in smaller deals too. This did not represent good business for the company because deals did not capture all the potential value that the company’s products and services provided. As a result, their pricing decisions eroded profitability.
The team knew a change was in order and they began to look at their assumptions and evaluate their data based on customer behaviour. The result was a different approach to sales engagements based on what customers were willing to pay for additional services and specific delivery options. Sales staff were also able to use more accurate volume pricing information as part of their negotiations. By rethinking their relationship with each customer, it was easier for the sales team to justify their pricing decisions. The result: increased profitability and more satisfied customers.
So how can you apply some of this approach to your own organisation? Here are four suggestions to help build effective segmentations:
Start with the question you want to answer. This includes looking at one question at a time. Start with a question that is strategic to your business. For example, looking at why deals will close this quarter is a different type of question than finding out the reasons why customers buy additional products or add-ons.
Measure yourself. Using data to understand your markets implies you have a way to quantify different market responses. This is essential for setting up the right key performance indicators (KPIs) in the first place.
Look for markers that change the KPI. A good segmentation strategy reveals how different market segments perform in certain circumstances. Finding those key attributes can show where new strategies are required and provide additional sales opportunities.
I have seen organisations use simple changes to affect customer perceptions on offers, products and services. However, finding those KPIs is reliant on good data analysis. Be sure to check your data sources to identify which attributes could be used, including your CRM database, customer master data, product master data and sales transactions.
With this data you can look at how KPIs are affected by changes in attribute values such as purchase frequency, geography, customer size, industry type, product category or product cost. Using statistical methods and/or machine learning algorithms will also help find those potential attributes.
Combine attributes to form segmentations.
Once you have created your segmentations, each one can have a specific set of actions for sales situations. With data analytics, it’s possible to provide more predictive guidance on those customer segments.
Segmentation is not a new skill; what is new is the level of granularity that is possible with prescriptive and predictive analytics found in every company’s Big Data.
It’s important to point out, however, that with all the data, there’s no substitute for the skill of selling. For example, segmentations that work well for up-sell or cross-sell situations may not work for promotional campaigns. To understand the difference and respond to those different situations, look at your data and analyse it, where possible, on a campaign-by-campaign basis.
As companies grow, the number of potential variables expands as well. Companies can potentially have thousands of different products to offer across multiple countries and regions, coupled with service and post-sales support offerings that also have to be positioned for the customer.
To retain control over this, sales and marketing analysts should look at how they can automate and manage their sales, segmentation and pricing decisions using Big Data.
From testing new attributes to automatically updating information inside a segment, this technology can help sales sell smarter but also faster and easier.