Predictive + Adaptive Models: How to Improve Multi-Channel ROI

The three models with the most impact for an awesome integrated campaign.

If you’ve been tasked with managing integrated marketing campaigns and you feel overwhelmed, you’re not alone. Marketing managers often suffer from data overload and use the wrong metrics to drive their integrated online marketing programs, missing valuable opportunities to increase engagement and ROI. This is certainly true for larger corporations, but even smaller companies suffer from metrics and analytics overload and need actionable solutions.

In this column, I’ll give a short overview of marketing models within an integrated framework designed to more easily and effectively boost ROI and create sustainable connections with customers.

Narrowing Options for Better Campaigns

Managers are faced with endless models and tools for integrating campaign elements across channels. This presents a problem for the “experimenter” type of manager, who tends to use an arsenal of tools. This breeds disorganization and lack of actionable analytics. For successful integrated campaigns, I encourage you to keep processes and tools minimal to create more agility and to stay one step ahead of market changes.

I’ve interviewed a number of companies and uncovered various models for awesome integrated campaigns. In an effort to narrow the options for managers, I’ve distilled the various methods into the three with the most impact. The three models to success involve a customized version of:

No. 1: Building predictive models. One of the hottest trends in improving conversions across channels is a process called predictive modeling. Predictive modeling for conversion is good up to a certain point – its major downfall is that it’s a guesswork method. That said, as a way to better meet the needs of website visitors, a carefully constructed predictive model is a powerful way to create empathy with visitors.

In most companies, the buzz word is “engagement.” The advantage of the predictive model is that it can open up the human factor in engagement, by forcing the team to create content and programs that express true empathy for visitors, which is the best way to authentically engage with them.

The power of the predictive model lies in the preparation phase. However, to create a useful predictive model requires time, budget, and training. Don’t expect your team to just figure out how to create a predictive model, or you’ll find an overabundance of guesswork in your prediction of customer’s purchase behavior. However, the payoff in customer empathy, engagement, and conversion is invaluable.

Pro: Helps companies turn around perception issues (lack of value, trust, security, or relevance) to increase conversion.

Con: Inadequate training could create a very wrong predictive model, actually creating impediments to conversion and engagement.

No. 2: Executing an adaptive process. As website analytics software grows more sophisticated, executing adaptive processes becomes necessary to just be in the game. Adaptive processes involve translating analytics into actionable changes. You see exactly what users are doing on the site, and you adapt your campaigns to more effectively help them reach their goals. It allows a company to get very close to the same kind of in-person interactions a sales person has, with continuous A/B adaptations of campaigns (based on analytics data).

Pro: Allows for a much more personalized experience, based on the user’s actual interaction with the site or app.

Con: Adaptive metrics for campaign creation are resource and time intensive. Both predictive and adaptive methods have a tendency to focus on less important metrics (not always ROI and engagement).

No. 3: Tracking channel effectiveness. Multi-touch metrics can properly attribute social channel conversions and ROI. In an integrated campaign, this is a necessity to measure which channels produced the most engagement, and ultimately the highest conversions.

Pro: Budget can be correctly allocated to marketing programs with the highest success rates.

Con: Difficult to implement.

Creating Your Own Framework

I’ve learned time and time again that the companies who are thriving in integrated campaigns are implementation-focused, sharing data gained from analytics to connect better with visitors and customers. The companies who are struggling tend to be more tool-focused than customer-centric.

If you’ve tried any variation on these models in your marketing programs, I’d love to hear about the results these methods had in your ROI and engagement.

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