Imagine if businesses could flick on a switch to gauge customers’ future buying patterns, make enterprise-wide decisions with absolute certainty and adjust risk levels to maximize customer profitability. This switch would help companies achieve the highest return from every interaction. In reality, this switch is a mathematical process called marketing optimization, which extracts and distributes customer data across multiple channels to match the right customers with the right touchpoints and prices.
The main benefit that marketing optimization offers, says Claudio Marcus, research director in the CRM and business technology practice at Gartner in Stamford, Conn., is an understanding of best customers, by predicting which customer groups will respond to a given offer at a given time. Optimization allows a marketer to go through an analytical process and change the numbers subtly or significantly to increase the likelihood of achieving his goal.
Optimization also implies doing that process over and over until the “optimal” outcome is achieved. Marcus says, “If I say we’re going to try to sell this product to this group by doing the following, and one quarter of the way through, we find that we’re getting a greater than or less than desired response, we can change the model.”
According to Bob Moran, research VP and managing director of data knowledge and analytics at Aberdeen Group in Boston, says marketing optimization often is confused with broader marketing tools. “I believe optimization enters into the conversation when you take the combinations of campaign management, marketing automation, predictive analytics and regular decision support, create a holistic view of what you’re doing for the marketing enterprise, then start defining your techniques and strategies for customers,” he says.
Two approaches to optimization
There are two approaches to optimization, according to Marcus: the “bottom-up” approach, which focuses on creating efficiencies at every point of interaction, vs. the “top-down” method, which is a macro perspective, defining the best way to allocate optimization resources.
In the top-down approach, companies such as Veridium and Mercer Management Consulting push software that integrates with a company’s data sources, prepares the data, then provides the ongoing ability to leverage and deploy models.
Consumer-packaged goods and pharmaceutical firms have the money to spend on such models. On the bottom-up side, companies such as MarketSwitch and Unica, as well as some smaller campaign-management and data-mining vendors, are developing models to embed into software applications for optimization at a transactional level. Financial services, telecoms and high-tech firms are using this approach. Today, the two approaches are beginning to come together, Marcus says.
Optimization models may include historical spending from different marketing channels, market-share data and competitive data on the spending side that all help brand managers gain a better understanding of what drives their businesses.
Optimizing, however, is no easy task, and is still lacking in most corporations. “These types of technologies are not for the shallow of pockets,” Aberdeen’s Moran says. “Start-up is expensive, but the paybacks are enormous. Some companies are saving or paying back the cost of their analytic tools just in postage saving from notdoing untargeted campaigns.”
Lots of changes ahead
Optimization tools will become increasingly more important, but Marcus says to expect vendor consolidation. The software will become easier to use and more affordable. Also vendors will tighten integration with source data applications.
Simulation software will become popular and eventually evolve into scenario-planning tools, where marketers essentially will test different scenarios to decide the best ways to invest in and monitor their resources to produce the desired results.
Finally, companies will start to engage in portfolio management, not only examining opportunities, but the risks associated with particular segments, as well.
Author: Mila D’ Antonio