Any business that performs extensive marketing understands the importance of lift. It's the essential metric of many campaigns, as it determines how many purchases came from the campaign as opposed to standard sales. Performance analysis software can be a great tool in assessing lift, for it helps identify key factors that distinguish standard leads from those that heard from the business through the promotion. At the same time, it's necessary for a business to identify key elements of their metrics can help them fully understand their lift. Otherwise, they risk creating a situation where they can't trust their own measurements.
Finding a critical starting point
The key issue that exists in determining lift is creating the baseline. Akin to what scientists or researchers would call a control group, MarketingProfs considers the baseline as the number of sales that occur absent any marketing. The basis of the pre-post analysis, which is where lift gets its formulation, requires a number of sales that would historically make sense. In that way, it's easy to determine the difference and subsequent impact of a new campaign.
"There's no single factor that determines sales conversions at any time."
There's just one problem with creating a baseline: Sales are extremely variable. There's no single factor that determines the number of sales conversions a company or store gets at any time. Examples of different issues are weather conditions, economic situations within the context of a single region, competitive marketing, establishing contracts with certain entities and product life cycles. All of these can very well influence the overall lift. It could turn out, for instance, that there was an overall lift to sales, but because the timing of the campaign occurred in conjunction with a change in the season, there's still a decline.
Getting analytical on the lift
In order to properly assess lift, baseline alone isn't enough. A business will need to both strip away the factors that can muddy the sales picture and add direct elements that make things a little clearer, according to retail marketing firm CrossCap. This includes removing cannibalization, which is when sales of one item declines because a similar product has a promotion, as well as the pull-forward effect, which occurs when consumers change their purchasing patterns to reflect a specific campaign.
All of these statistics require some level of analytics software. IBM addressed this in releasing its Lift Analytics for Retail platform earlier this year. Its focus on specific pre-built metrics and models that retailers can use to identify all the aspects that help determine the success of marketing campaigns. With these types of tools, it's much easier for business to gain an understanding of lift and market better.