Big Data has been a hot topic here at SpeakingDIRECT and across the direct marketing industry. We’re excited to have one of our first guest bloggers back. Seth Goodman, EVP Client Services at DataLab USA, walks through how direct marketers can take advantage of Big Data in a direct mail campaign by having clear objectives and starting small.
Big Data is a big topic in the marketing world. Many industries have successfully used it to create more targeted messages for their audience, which, in turn, has helped drive response and sales. However, when it comes to direct mail, Big Data is still generating confusion regarding how to effectively implement it into a campaign. Some of the questions DataLab USA gets on a regular basis include:
- How much should I invest to make Big Data usable for direct mail?
- What type of Big Data can be used to help boost direct mail results?
- What advantages does Big Data have over the data normally used for direct marketing?
- What type of statistical packages do I need in order to apply Big Data to my marketing campaigns?
Most companies are struggling to use Big Data in a direct mail setting because Big Data, as it is structured today, doesn’t necessarily lend itself to the constructs of mail. The data can be disorganized, stored in separate silos and difficult to abstract meaningful usage.
However, there are opportunities to use Big Data for direct mail:
- Developing response models based on transactional behavior from other marketing channels, such as online;
- Crafting messaging to prospects at a very granular level;
- Honing in on the right time to mail a prospect based on other data sets available in the organization.
Marketers are only scratching the surface of how Big Data can be used in direct marketing. With that in mind, our advice is to start small. It’s important to identify just a handful of specific objectives for your direct marketing campaigns. These could be outcomes, results, tests or metrics. Based on these objectives, you then begin to identify what kind of Big Data might be available to help guide the process. In some cases, this may be an iterative process working with internal analytics and IT teams to identify data sources and elements.
Once the data sources have been identified, the statistical analysis part of the process should begin. With the end objectives in mind, the analytical teams should attempt to extract meaningful findings from the data sets that will result in a positive lift in ROI.
As appropriate statistical findings are recognized, classic direct marketing tests should be designed in order to assess the impact of the additional data versus business as usual. The tests should be designed to provide statistically relevant outcomes; otherwise, the impact of the Big Data (and the investment in it) cannot be determined.
The usage of Big Data for marketing should be a continuous process. The rapid pace that data continues to grow requires revisiting any marketing strategies implemented. This helps to determine if the existing strategies are still working, and if any new data sets have been identified that can provide significant lift.
While the direct mail industry is still trying to work out the kinks in how to use Big Data, there are opportunities now to make it work for your next mailing. By having clear objectives and starting small, you can turn Big Data into an even bigger marketing opportunity.
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