Reduce Costs AND Raise Service Levels
It can feel like a tug-o-war between controlling costs and keeping customer service levels.
Front-line managers (like store managers and buyers) especially are in a vice between cost cutting and customer service. Keep plenty of inventory and higher service levels, or lower inventory and risk disappointing customers? Keep more staff in stores, or save cost and risk leaving customers waiting?
Those managers have a good instinct for what drives the business - weather, seasons, etc. - but they have limited time, data, and tools to plan.
That tug-of-war can wear out these key people.
The solution is to avoid the tradeoff altogether. Your data contains all the ebbs and flows of business, and AI can reveal the patterns.
We start with all available transactional data, down to store or category, and hour.
Other business drivers - promotions, holidays, weather, seasonality - add depth to the picture
We craft a model of your business using AI that predicts foot traffic, call volume, or sales volume.
Put that demand prediction in the hands (literally ... on their phones if you like) of front-line managers
We’ve even maintained accurate predictions through the business fluctuations of the COVID pandemic.
In around 12 weeks, you can stop the tug-of-war
Know the drivers of your business
Know precisely how much volume to expect, displayed on one screen
Empower your managers to spend less time on planning and adjusting (rush orders to re-stock or calling employees in / sending them home) and more time for customers
This consumer services retailer who works with Pivot Point now has accurate predictions of foot traffic, by location, day, and hour.
AI algorithms take history, holidays, and weather into account.
Beyond the whiz-bang of AI, we make sure results are put to use. In this case, the forecasts are fed to a dashboard each store manager uses to make labor schedules, along with alerts of changes. They can reduce cost by not over-scheduling, and increase service levels by not under-scheduling.