The ROI of Campaign Response Optimization with Predictive MarketingMarch 08, 2018
As marketers, we often make the mistake of mailing too many people and often have difficulty determining how far down in our lists to market. It’s a question that we ask ourselves with every campaign, and the problem isn’t going away. Most campaigns have thin ROI margins to begin with making it far too easy to accidentally sabotage your own campaign with bad targeting and wasted cost, which is why it’s so important that marketers optimize their campaigns based on predictive response modeling.
Optimizing campaigns by mailing to your most-likely responders has not always been easy (until Reach!); in fact, in many cases the process can takes days, or even weeks, and requires teams of data scientists. That’s what sets the Reach platform apart. Our predictive marketing platform identifies your most-likely responders by tile—quickly and easily—so that anyone can identify exactly when to suppress targets and cut future losses, no data expertise required.
Let’s look at a past, real-life customer example.
In this instance, the customer decided to run a direct mail campaign to 1,000,000 names. For their campaigns they typically saw a 0.50% response rate, had a price-per mailer of $0.90 and an acceptable cost-per-sale of $150.
The customer mailed everyone (all 1 million names) in their initial campaign and got a total of 5,000 responders. In order to get the most value out of their list, the company prepared to run a second campaign.
Normally, this customer would continue to send second, third, etc. campaigns out to all of the prospects, including those who hadn’t responded to the initial campaign. This time however, prior to their second campaign, they used Reach’s predictive response modeling to score their list and get a breakdown of their audience by propensity to respond to this particular campaign and offer.
A “tile” is just 10% of your scored responder list. Tiles help you identify at what volume your marginal revenue equals your marginal cost and what list cut-off point will make your campaigns most profitable.
Rapid response modeling allowed the customer’s marketing to calculate for themselves, in minutes, whether or not is was profitable to mail parts of the list again, or if the cost of garnering a response would be too high, given their acceptable cost-of-sale.
With predictive scoring, they learned that of the 995,000 non-responders from their initial campaign, only the top 20% were worth mailing again, the rest were best suppressed.
By optimizing their campaign and predicting likely responders, this customer was able to achieve a completely different cost table (Figure 3.0) for the remainder of the campaign! Before optimization, they had a negative campaign ROI of 16.67%. After optimization with Reach predictive, the campaign returned a 31.67% positive ROI (Figure 4.0), while only spending 20% of their original $900 thousand budget.
(To increases volume and smartly invest the now surplus campaign budget, the customer used Reach’s predictive look-alike modeling to backfill the suppressed names with net-new names most likely to become their customers. But more on that topic in a future blog post.)
Getting More Bang For Your Buck
Consumer marketers and their agencies expect significant campaign optimization today. It’s too easy to waste money on campaigns. Modern marketers have to move beyond traditional list filters for targeting, which simply fail to achieve profitable campaign results in too many cases.
Marketers and agencies know that they need to get away from simple filter targeting and implement more advanced predictive modeling in order to target their marketing efforts on their best prospects, but the hurdles were high before Reach.
Implementing predictive consumer targeting usually means having to hire a large team of data scientists and doing things manually, which does not scale (especially for agencies where manual data science teams just can’t become a truly profitable predictive service offering as the marginal costs are too high). If they want to automate, companies have to implement a big, custom enterprise predictive analytics solution from a behemoth provider, which is cumbersome and very costly and still requires data teams to use and manage.
This is what sets Reach apart. We offer a self-service predictive platform made specifically for marketers that allows mid-size brands and their agencies to get all the benefits of ultra fast, scalable predictive marketing, without the overhead of a large data team and without the added cost and hassle of installing huge customer predictive analytics platforms.
Any brand and agency can get started in minutes using super fast predictive marketing with the Reach predictive cloud. With zero implementation, you can start optimizing your campaigns now to save money and create real ROI.