So far in this series we have been looking at scoring as a tool to make sales more productive in following up with leads. But organizations are putting scoring algorithms to work in numerous other areas to improve the performance of their people. This article will close out the series by taking a look at how scoring is being used in pre-qualification, marketing, call centers (aka contact centers), human resources and other areas.
This article is the last in a four-part series. You may find it beneficial to read the first three parts before diving into this one. Here are some quick links and summaries to help you out:
In Part 1, we discussed the concept of scoring and how to get the right data as an input to developing your scores.
In Part 2, you learn about the different scoring options from simple to complex.
And in Part 3, we explored how to work together with sales to better help them understand lead scores so that they can more effectively engage in a dialog with a prospective customer.
To summarize our first 3 parts, scoring is about using The LUCK Principle(TM). You listen by capturing data into your CRM or other database about customers and prospects. You understand by using predictive analytics to convert that data into a score that ranks a person with their likelihood to respond. You connect with a more targeted group and with a more relevant message (based on what they told you when you listened). And you know the results afterwards, which helps you to get smarter and improve on how you listen, understand and connect the next time.
With that in mind, let’s take a look at other ways that predictive analytics and scoring is being applied across all parts of organizations.
Your marketing team may be able to make even better use of scores than your sales team can. Scores can be used for any kind of targeted communication including: email, direct mail, outbound calling and increasingly can even be applied to social marketing. Most marketing teams frequently reach out to existing customers – which can provide a much richer data mine because you know so much more about your existing customers than you do your prospects. For example, you should know how recently your customers have purchased, how often they purchase, how much they spend, and what products (or product categories) that they purchase from. This can help you to not only fine-tune your targeting, but it helps you serve your customers by interacting with them only in the most relevant ways.
Thus far we have really only talked about scoring prospects (or customers) based on their likelihood to respond. But scoring can also be used to qualify how good a customer is likely to become. You can use predictive analytics (described in part 2) to predict the total lifetime value of a prospective customer – thus targeting only the individuals who are not only likely to respond, but who are likely to become a loyal lifetime customer for your organization.
Along those same lines, qualification models can be applied to other areas such as: likely energy consumption (to aid with capacity planning), prioritizing government response based on severity (to aid in responding to citizen requests for government services), and predicting demand based on external conditions (aiding with preparedness based on environmental events).
Most organizations offer a broad array of products and services. These organizations want to do more than score someone for a product-focused campaign. They score an individual for the next product or service that they are likely to need. Using this approach, you can send targeted communications focused specifically on what the customer is likely to see as the highest value offering from you. Not only do you reduce your marketing costs by not sending multiple messages, but you also reduce “contact fatigue” on the part of the customer (making them more loyal, and less likely to opt out of receiving additional messages from you).
One of the great things about a contact center is that you are gathering more data all the time (continuously listening to customers and prospects). So you can improve your scores almost in real-time. Some of the ways that scoring is being used in the contact center:
Did you know that it costs as much as 10 times more to win a new customer than it does to retain, grow or re-activate an existing customer? This is even more true in the business-to-business world where brands are not generally as well known as they are with consumers. Scores can be used to identify which customers are most likely be come back (or re-activate their relationship) if you reach out to them.
An unfortunate reality is that some customers simply do not pay. Some may be unethical and make a habit of not paying for the value that they get from other businesses; and others may hit upon unforeseen circumstances and be unable to pay. Risk scores can help you predict which customers and prospects are likely to become an issue from a collections perspective. Risk scores for businesses can be purchased (such as from Dun & Bradstreet). But you may also find it helpful to calculate your own risk score (D&B won’t have the customer history data that your organization will have – so use that to competitive advantage).
Risk models are also regularly used in insurance and financial services. Risk model scores can be used to adjust pricing, interest rates and credit terms.
Many of the same scoring approaches described above work just as well in the non-profit sector. You can score individuals for if they are likely to become donors, to identify how they are likely to donate (will they be financial, volunteer, material donors, or some combination of the three), and frequency (will they donate one time, or are they likely to be frequent donors) and, of course, to also identify donors who are likely to reactivate.
A lot of internally used social collaboration systems have scoring built in. This takes the form of “ratings”, “likes” or other adoption type scoring. This kind of scoring can be used to help your team to work together better internally, assist in finding the most relevant information to solve a business problem, or can aid in change management by making it fun to adopt new processes and technologies. Many organizations use this type of scoring to identify and prioritize issues that are important to their team or to their customers. A great example of collaborative scoring: check out My Starbucks Idea.
Organizations are increasingly aware of the high cost of replacing their best employees. This is particularly true in healthcare and information-based organizations (technology, banking, etc). Scoring is commonly applied to employee satisfaction feedback and employee engagement data in order to not only predict which employees are at risk of leaving but, more importantly, to address the most critical issues that have the greatest impact on employee satisfaction and retention.
For those who are new to it, scoring can seem a lot like “big brother”. It’s reducing people to a set of numbers! In fact, just like any other tool, scoring can be used in an unethical manner. One way of thinking about it is to consider the personalities of people. Some people are genuine in wanting to build relationships; others are manipulators and only interested in what they can get. Scoring can be applied in the same manner.
Used properly, scoring is a way not only to optimize your organization’s budget, but also to serve your customers, prospects and employees by trying to respond to them in the most relevant manner possible. Think of scoring as a way to simulate an individual prioritization process across thousands – or even hundreds of millions – of people, all at once.
In other words, at it’s heart, scoring is about people. The numbers are there to empower people to work together better – inside and outside of the organization.
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