Why retailers have had to adapt to accurate data reporting
Retailers have a need to manage data more than ever now that all of us want to shop 24/7 and we expect offers that match our buying patterns. I recently went into a local store, had an excellent sales person that was able to source an item for me online and I bought and paid for the item knowing it would be delivered to the store later that week. Unfortunately the stock management system was not accurate. The data reporting infrastructure was not able to support the great sales person in-store and it meant that the customer (me!) felt frustrated.
Supply chain management reporting
This kind of ‘real time’ data reporting isn’t easy. Retailers have to match their stock to purchase records (till receipts etc), to location codes (where the stock is housed) and then provide on and off-line shoppers an accurate record of stock levels. From a data point of view the stock can be assigned codes, it can be given locator indicators (using bar codes) and this can be cross-checked with till receipts. The reporting can be done in batch processing and this can be carried out as often as the infrastructure in place can handle the processing power required. But there will be gaps. There is the obvious time lag with batch processing but more critical still are the delays in stock levels that arise from the ‘people’ part of the supply chain process. A lorry load of stock arrives at the warehouse and in the hurry to unload it the new stock does not all get scanned. Returned stock to shops gets scanned but the wrong code is assigned to the reason for return and the stock is categorised as faulty. And so on…
This is where a universal stock pool can be of help. This is the ‘gold standard’ of retail stock management and industry estimates are that only 20% of UK retailers currently operate this in real time. For goods with a short shelf life this gets even more tricky (further reading: Economist article on ‘croissantonomics’ and the extra burden of stock control for retailers of perishable goods).
Customer acquisition and retention
Retailers have to match their online and in-store customer information. But wait…who are the in-store customers? Transactional data is much less informative for retailers than an online purchase. As a retailer do you have a loyalty card scheme, do you administer the scheme in-house or do you outsource it? Do you have ready access to the loyalty data and is it linked to your offline and online purchasing systems to identify your regular high value customers? So many questions and so much transactional data don’t make this straightforward.
Without this data retailers can’t to get to know their customer. In-store colleagues would benefit from knowing when a high value client (not customer – I will come back to that later…) walks into store. The online and loyalty scheme behaviour and purchase journey data in itself is not good – it is only actionable insight when it is collated properly and delivered to the right analyst or sales colleague in time and when relevant.
Customer to client
Once your data capture, data quality, data management and data delivery is managed effectively then you can start to measure and assign value to your customers. By getting to know your customers you can start to proactively engage with them and create a dialogue. If you have a mutually beneficial relationship with your customer they become your client, your regular and your brand advocate.
IT is often too busy keeping the lights on to innovate
There is a conundrum facing retailers wanting to increase their use of customer data. The infrastructure that surrounds data storage comes under the remit of IT; but the demand for use of that data is coming from all over the business. IT teams are trying to balance security, whilst maximising processing speeds as well as support a complex hardware infrastructure. There is an understandable reluctance from IT to experiment with analytics without factoring in these competing demands. This can lead to frustration from business teams.
I asked seasoned data integration analyst, Ian Cray, why not walk away when a client’s organisational problems seem so insurmountable even before he starts to tackle extracting meaningful insight from their data?
All problems have solutions. I like challenges. Often the client cannot see the wood for the trees so an external view helps. I am old, so whilst these problems appear new to the client, I have often come across them before.
Ian explains that he starts with an audit of the retailers current customer journey, mapping out all of the individual touch points to identify the gaps and the data bottlenecks. Another key area for focus for retailers, Ian says, is the customer complaint and customer returns. This data should be easy to access throughout the business to help inform colleagues. Measurement in this data-driven environment is the key; measure everything. This requires consistency of data so this is where data quality and data management are essential. See how good data management drove data-led growth for Monsoon Accessorize in this case study.
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