Every marketer is faced with a common challenge: improving conversion rates of your marketing campaigns whilst optimising marketing spend. This sounds straightforward (or so every non-marketer thinks), but is actually much easier said than done, at least when using traditional approaches.
We, however, are lucky enough to live in the Age of Data. My experience working with clients across the globe, has shown that marketing leads that have been selected through an analytical driven strategy using predictive modelling are more than double as likely to respond to your campaign.
In this three-part series, we will explore the use of advanced data analytics in lead selection and acquisition strategies and the impact it will have on your acquisition rates and ROI.
Why do analytics improve results?
When using a large database for a marketing campaign, a key objective is to identify and remove high risk or ineligible prospects, in order to minimise time and resources spent on leads that won’t result in ROI. You need to eliminate those who are unlikely to be interested, and in doing so you will improve your response rates: making your campaign more profitable. You also want to identify the leads who are most likely to become good, high-value customers, in order to focus your efforts on the leads that will improve your company’s bottom line – and the longer they stick around, the better.
Without data analytics, all you have is a list of names and a lot of information associated to each lead. But with a data-driven approach, you can identify marketing lead lists made up of prospects who will be worth your while, and lists containing the leads which you should rather not focus on. You can rank your universe according to the measures important to you and select the best set of leads.
What will change with a data-driven approach to lead acquisitions?
There is a lot that will change when you implement a data-driven approach to your lead lists. Focussing on high-quality leads, as you’ve no doubt experienced before, results in higher response rates, which means your campaign will have a higher success rate. This in turn results in more business for your company, and most likely better quality business: high-value, repeat customers, who don’t miss payments, lapse early or churn easily.
If you start your marketing campaign with a high-quality lead list, your leads are likely to respond sooner, which means you have to spend less time and resources to close them into paying customers. This will reduce your average cost per new customer.
As a marketer, you likely also use multiple channels to target your prospects. By identifying the channels that leads would be most likely to respond to, data analysis also provides a platform for improving channel cost per acquisition, through improved channel allocation that can increase response rates and reduce costs.
How and in which areas can data analytics be applied to identify high quality leads for acquisition campaigns?
Principa have used analytics to profile leads based on likelihood to respond, and repay. These analytical models have been used across various industries to identify prospects for marketing campaigns.
For e.g., retailers use these models to identify prospects that would be likely to take up a loyalty or store credit card. Insurance companies use data models to find prospects who would be interested in an insurance product, but could also afford the monthly instalments. The same is true for Telecommunications and Loan companies.
Response and risk are highly interrelated when using a data-driven approach to marketing, as you will often find prospects who are highly likely to respond to an offer, but unlikely to prioritise the repayments or monthly instalments. It is of utmost importance that prospects are evaluated by both these measures or even the overall potential profitability of the lead.
The predictive models that we develop, profiles prospects based on likelihood of response, as well as on the risk that they present to our client. Both of these factors, together with policy exclusions are considered during the selection process.
Channels can also be a factor in the selection strategy to identify the channels on which leads are more likely to respond.
In my next blog, I’ll talk about the finer details of data use and compliance, with regards to marketing strategies. If you’re interested in learning more about generating lists of consumer leads most likely to respond to your campaigns using advanced analytics, read more on Genius Leads or get in touch with us to ask any questions or talk about a solution for your company.