There will always be customers who buy the latest product from a brand as it becomes available, whether it is the latest Harry Potter book, iPhone or sports shoe. Equally, there are customers who do their research and know precisely what they want to buy, down to the model number. Their only decision influencers are speed of delivery and price. However these people are not the majority. Most shoppers know in broad terms what they want – a new washing machine, a book to read on holiday, a smart phone, but they need, and want, to be guided to a product that will, in their terms, deliver value for money.
So the key is knowing what, for an individual customer, makes up 'their terms'; a mixture of budget, style preferences, need to see before purchase and so on. Therefore, we need to tag a customer at the start of their journey of looking for a product, link their purchasing history to them, be aware of their browsing and social media history, which might also show aspirations or peer pressure and ensure that we can point them in the right direction for appropriate purchase options. Great in theory, but how close are we to being able to do it?
The two problems with turning theory into practice are aggregating everything we know about a customer into one place and having this information available in or near real-time to influence the purchasing decision.
What has changed in recent years is the advancement in big data processing capability. We now have access to technology that allows us to collect every little snippet about a customer or potential customer without restrictions on volume, variety of format or source. Structured data, such as a customer’s web browsing and purchasing history, can be added to unstructured data like pictures and videos or social media. We don't have to predetermine what information we want or how we will use it, we can collect everything against the possibility that it might be useful in the future.
New streams of data are arriving all the time and the amount of data available will grow exponentially through the Internet of Things (IoT), as everything from fridges to cars become routinely connected to the internet. While all this information is useful for trends analysis, it becomes really powerful when linked to the individual customer. When you can ask what their previous journeys to a purchase have looked like. Did they browse online or in-store or both? Did they look at social media to support their decision? Discuss brand options with friends on chat forums? Were they influenced by warranties and add-on offers? Where did they buy?
A lot of this information is most useful if we can interrogate it while the customer is on our website or in our store. Knowledge of their buying preferences can help us to offer the customer the most relevant products; child's bike or racing bike, budget line or mid-price range, availability of other models on-line, the product they really aspire to. The return of genuine customer service and knowledgeable support to their buying decision, which for so long has disappeared from the retail scene as retailers and shoppers no longer have a personal history together. Big data based systems enable real-time interaction with a customer's data while they are on their journey to a purchase on-line, on their mobile or in store.
In order to take immediate action on in-the-moment analytics, the data has to be read by a marketing application. That’s because being able to react instantly requires pushing digital intelligence into content management systems, dynamic ad-serving engines, and the marketing ecosystem as a whole. Customers are moving quickly and if the system is not able to push digital intelligence into the ecosystem efficiently, valuable opportunities will be missed. An online retailer or online media outlet could enable content curators to change the featured products and articles on the fly, based on interest or preference. As-it-happens data would provide immediate insight into the impacts that those changes made. Pricing and position on new items can be changed with immediate feedback to test elasticity, a key value for launching new products and content.
If we step back a little in the process, the customer can only purchase what is available. We have all heard of, if not experienced or even been responsible for, customers wanting to buy a product on promotion that is not actually available because it has run out of stock in the shop and even online. Real-time data streaming from our behavioural analysis into the stock control system can provide as-it-happens and where-it-happens information on customer response to a campaign. Browsing interest and conversion rates can enable stock control to have stock in the right place or on order and even allow marketing to suspend a campaign if demand starts to exceed supply.
Behavioural analysis, enabled by big data-based systems, ushers in a new era of customer retailer relationships. Real-time response to both what is selling and customers' buying journeys means that the customer experience can be enhanced to a true win-win happening. Retailers can provide appropriate guidance and support to their customers’ achievement of buying satisfaction.
By John Fleming, marketing director EMEA at Webtrends
PrivSec Conferences will bring together leading speakers and experts from privacy and security to deliver compelling content via solo presentations, panel discussions, debates, roundtables and workshops.
For more information on upcoming events, visit the website.
comments powered by Disqus