App & SaaS
Optimize your user acquisition costs and maximi se your lifetime value (LTV)
Sample results from our software customers
smaller customer aqcuisition costs (CAC)
Increased sales for current customers
Optimizing UA investments from a download to LTV
Sellforte suits perfectly for grocery retail industry due to its massive amount of data and complex media strategy with overlapping campaign effects. Optimize your investments across store concepts, campaign types, and media mix. Use cases include both media and promotion effectiveness to cover broader marketing strategy optimization.
Important factors like price promotion elasticities across product categories and items, seasonalities and industry trends are taken into account.
Main use cases
Cross market optimization
The biggest wins in user acquisition optimization can be done by optimizing investments across markets.
Since the total uplift of campaigns has typically been hidden with other solutions, user acquisition (UA) investments are not divided optimally to maximise growth.
The model finds market-specific baselines, uplifts per campaign and recommends allocation by effectiveness. All you need to do is the adjust based on your targets.
Big brand campaigns affect all store concepts like Hypermarket, Supermarket and Market concepts, but not evenly. To make sure that huge investments to branding campaigns are not just fluffy budgets, that just needs to be spent, you need extremely robust modelling to capture uplifts across store concepts to define the campaign ROI.
Trials to paying
Weekly, seasonal, branding, store concept, monthly, and eCommerce campaigns. If you try to treat these campaign types in the same way from a marketing effectiveness perspective, you'll find yourself in a mess. Robust modelling between different campaign type decays lets you optimize investments across campaign types and get more bang for your bucks.
The optimal media mix for a campaign is always related to the advertiser's market share, campaign type, and campaign targets. To be able to find an optimal media mix, one should have enough historical data from past seasons, but also multiple campaigns with variations in the media mix. Variation is fuel to the modelling that lets it find uplifts that mediums create.