For some parts of mobile shopping, it’s all about the
numbers.
Anyone tracking mobile shopping behavior is aware that the data coming from shopping activities is starting to look like a tidal wave.
A major report has taken a look at all
of this data and points out that the retail analytics objective is to make better operating decisions and improve the shopping experience through the intelligent use of information.
One of the
problems is that linking all of the relevant data is hardly an easy task, according to the report by the Platt Retail Institute commissioned by Tyco.
While every consumer digital interactions
leaves a digital footprint, tying all those footprints together can be daunting. And that’s where data analytics comes in.
The report cites numerous customer-driven types of retail
analytics, such as:
- Segment, target and personalize offers
- Identify customers with the likelihood to spend more and the categories for that spending
- Identify
customers likely to shop elsewhere and discover how to retain them
- Understand which customers represent sales and margin increases (or potential losses)
- Analyze buying patterns and
preferences by segment
- Analyze price and profitability
- Gain insights into performance drivers
- Analyze brand and sentiment
- Recommend product offerings for
particular customers
- Understand social and other influences and how they impact customers
- Make adjustments in assortments, pricing, service, marketing communications or other
customer experience elements that can produce incremental revenue
- Gather information about and analyze customer influencers and understand how they impact purchasing decisions.
All easier said than done.
And when adding customer location-tracking technology into the mix, in-store traffic can be included. The report notes that this is critical since 94% of all
retail sales occur in a physical store.
The other critical factor is to make accurate inventory information available to mobile shoppers.
The number one experience most (73%) shoppers
disliked when shopping was an item being out of stock.
There will continue to be even more numbers and data around mobile shopping as technology and behavior evolve. The challenge will be to
identify and deal with all that data.