How organizations design and manage their offering will have an enduring impact on their competitive advantage, costs and lifetime revenues. The following are best practices for building a successful data co-op or MN, no matter the industry.
1. Make change management a priority
At the outset, it’s important to ensure that executives and all affected departments understand the value of the data co-op or MN to the organization and to individual departments, as well as how processes will change. Get buy-in from the heads of digital marketing, data and analytics, MarTech, owned experiences, digital advertising and digital transformation. Organizational alignment is crucial. For example, merchandising teams that have offered free digital advertising as a deal sweetener to suppliers who purchase physical marketing displays need to understand the value of the MN to the company, which ultimately benefits their team.
2. Set realistic expectations
Whether starting with a data co-op or MN, set expectations with all stakeholders that revenues may take several years to reach their full potential. For example, a RMN Publicis Sapient built for a major U.S. retailer brought in $24 million in revenue in its first year, which grew to $150 million by the fourth year—a 625 percent increase. In general, MNs require a significantly larger investment than data co-ops and bring in more revenue potential. Another Publicis Sapient client saw $5 million in year-one revenues from its data co-op and $100 million from its MN.
3. Establish a data quality management function
Data is like crude oil in that it needs to be discovered, extracted and refined before it’s taken to market. Whether starting with a data co-op or MN, organizations should invest in data modernization to improve data quality, adopting best practices for extract, transform and load (ETL) processes. This involves bringing in data from various sources, including the loyalty app, website, in-store transactions and actions taken in response to ad impressions. Identity resolution software, such as Epsilon COREID or LiveRamp, enables this while also stripping PII from customer data to comply with privacy requirements.
In addition, the following success factors are important to consider specifically for MNs:
4. Report on incrementality
With advertising options ranging from Google Ads to social media, why would brands and suppliers spend their ad budget on a particular MN? In a word, incrementality—metrics showing whether an ad impression seen by a customer directly resulted in the desired conversion action and how much of the sale is a result of the ad. Advertisers can benefit from a centralized planning portal for audience targeting and reporting, such as Publicis Sapient’s RMN Accelerator. Advertisers can use the portal to identify their target audience, such as lapsed buyers or households with two children under 18 and a certain income. They can also conveniently generate performance reports that compare conversions for different audiences, messages, calls to action and more.
5. Aim for shorter attribution lookback windows
Many of today’s MNs give advertisers a 30-day lookback window. But attributing a customer’s latte purchase to an ad seen 30 days earlier is a stretch. Similarly, data syndicates typically report on sales lift four to eight weeks after the campaign’s end, too late for advertisers to apply insights. To set MNs apart, businesses should design them to support a shorter—and therefore more relevant—attribution lookback window.
6. Share near real-time incrementality measurements so advertisers can optimize campaigns while they’re in-flight
Imagine a campaign aimed at successfully reactivating lapsed buyers of a chocolate bar brand. With real-time incrementality measurements, the marketer can run experiments to see if reactivated customers will continue purchasing the item without the need for additional discount offers.