Inventory intelligence
Upp’s Inventory Intelligence understands every product to drive its performance
Upp. takes in to account over 120 data points; product, business operations, consumer, & trend analysis to act & restructure in real time how retail channels operate.
In numbers:
0m
SKU’s managed
0bn
analyses per day
0k
Google Shopping changes per week
£0M
online sales enabled
Overview:
Upp. connects previously disparate data sets to form a unique decision making system for each and every SKU advertised on Google Shopping. This enables retailers to intelligently automate advertising decisions, capturing buyer demand when it’s there and wasting less budget when it’s not.
Instead of relying on customer data, Upp. combines this with business operations data (COGs, availability, price points etc), customer influencers (delivery, promotions etc) and detailed trends analysis. This knowledge is then matched with the retailer’s specific goals
and changes are made automatically on their Google Shopping campaigns. Upp. takes the guesswork out of trading and guarantees your marketing budget is supporting the right products at the right time with the right amount of investment to succeed.
Our decision intelligence analyses 7 billion data points per day, making over 48,000 adjustments on Google Shopping campaigns each week. Human teams simply can’t do this, in real-time, all the time.
And it’s this combination of data for each individual SKU that provides the power behind the ad automation and the insights which will continue to transform eCommerce: we automate on Google shopping now, but our solution will continue to evolve to transform all area of eCommerce from pricing to promotion and delivery.
Finding the correct price point is crucial to any ad campaign. Upp.’s retail intelligence makes price point recommendations based on multiple operational data points, allowing retailers to make informed discounting decisions.
The buy price, plus freight-in costs, COGs is a vital metric to determine the correct price point of a product. There is no point dedicating advertising spend on products that do not reach the desired level of profitability.
If a product is not meeting customers expectations, resulting in high return rates, then this needs to be identified before committing advertising spend.
Historical order data can provide valuable insights into the effectiveness of ad campaigns, as well as providing a focus on which products may need a boost.
Availability, or inventory levels will also determine the relative ad spend. Products that are about to go out of stock, do not need to be promoted, and Upp will flag this and adjust the ad spend accordingly.
Does your product attract impulse buyers, or bargain hunters. Knowing who is buying tells us how to reach them.
Upp. takes into account delivery options that might influence customers, not only in terms of cost, but delivery times that might deter a purchase.
Product reviews are one of the biggest influencers in customers purchasing decisions. Upp OS gathers review ratings before determining the amount of ad spend. There is no point promoting a poorly rated product.
Upp OS gathers promotion data to ensure that ad spend is closely tied in with promotions. Spend is automatically adjusted in relation to the duration of promotions.
The ad budget is the overall framework that the ad team is constrained by. All the spend on promoting different products has to fall within this budget.
How ads are performing has a significant input on future spend. CTR (Click Through Rate) gives vital data on ad impact.
Gathering product performance data from all sales channels to give priority and focus to top sellers.
Using historical Google Shopping bid data to define future bid levels.
Upp OS is gathering data on product performance, through all channels. Imagine getting real-time insight into what your competitors are doing, how their ads are performing, their pricing. Understanding your competition hugely impacts the success of each product’s ad strategy.
Site metrics can help understand how your product is performing in all your channels. Views, bounce rate, conversion rate all influence how much you want to spend on a particular product’s advertising.
Analyzing all web traffic relevant to a particular SKU, gives insights into, for example, geographical sources of product interest, which are fed into the decision intelligence.
Many products are seasonal, but when managing the advertising of 1000’s of products, this might get overlooked. Upp. doesn’t miss this.
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