How retailers can use cloud computing to drive sales
Cloud computing can sometimes be misconstrued as a toolset only to be used by system integrators and software engineers. But that view is now out of date – Cloud technology is transforming marketing as we know it, and it is becoming the basic toolset for all marketers today.
At its heart it offers the most efficient method ever devised to tell the customer a story, connecting his or her needs with the offers that we have created in-store and online.
In today’s world Machine Learning is needed to automate and deploy your campaigns directly in the suite of digital marketing tools, because that’s the fastest, cheapest and best way to get your message to the customer before they change their minds or buy the product elsewhere. Here’s how.
The new maths: Retailers love figures.
At every level of the company, retail operations are driven by people who look feverishly at performance numbers every day – seasonal trends, category insights and stock level variations – this analysis is critical to the success of any trading.
The most advanced retailers are looking at customer data in new ways that dramatically change the way they find trends and opportunities. They are using Machine Learning to spot the next significant improvement in their marketing and customer experience processes using test and learn techniques on huge volumes of very diverse data sets.
Traditionally, the analyst has defined the scope of the reports that we have used to review sales; today, new Machine Learning techniques are making possible the mining of data patterns amongst millions of interactions in the customer journey across a range of platforms that are beyond what the human can consider and at a speed that cannot be matched using traditional statistical methods.
This brave new world does not exclude the insights and intuition of experienced retailers, it requires both a good business knowledge and data science skills. But Machine Learning, supported by the muscle that can be provided by Cloud computing, will present you with insights and different processing capabilities faster, cheaper, better. Let’s investigate how.
What so great about: faster?
The real innovation here is not to be found in bigger reports, delivered to you in seconds. It is the ability to turn analytical insights into action.
The mainstream digital marketing platforms – such as Google Marketing Platform, Adobe, Amazon and Facebook – are built on a Cloud-based infrastructures, making it increasingly easy for marketing activities to leverage Cloud computing. This makes the promise of mass personalization a practical reality for the advanced retailer. By “mass personalization” I mean homepages configured to each individual customer’s profile; ads and emails that are based on previous decisions that specific customer has recently made; and offers that anticipate that customer’s most likely next purchase with an offer or nudge to close the deal.
The innovation here is the use of Cloud computing to turn the Machine Learning into triggered responses and tests, to use digital tools to actively sell to customers rather than let the customer do all the work. For example, Google Marketing Platform and GoogleAds and Facebook have TargetCPA and TargetRoAS algorithms which process data in near real time. Google Analytics has “session quality” which lets you create audiences with high likelihood to purchase or complete an action.
And every time the customer reacts or fails to react to our test, we get wiser, leading to better promotions next time round.
What so great about: cheaper?
Much of this targeting may have been achievable in the past, of course. But delivering messages tailored to the individual has historically required the marketer to undertake a painstaking process of crafting the data and creative in ways that are clunky and expensive, using traditional data warehouses that cannot possibly match the speed and randomness of the customer’s online journey.
The tests that have previously been feasible with existing CRM systems were limited by large numbers of analysts, marketers and integrators building campaigns on premise. It has always been expensive and highly complex to create an on-going testing framework for isolating and continually learning the tactics that close the sale most efficiently in the widest range of circumstances.
Using Cloud for this type of orchestration of campaigns and promotions, incorporating optimisations for future improvements, has an initial setup cost close to zero.
What so great about: better?
What could be better than giving the customer what he or she wants? By carefully curating and tailoring marketing processes to address the needs of the customer, we can deliver on the promises that we make when we ask for permission to market to consumers under GDPR. In return for access to their personal data, we are offering something of value.
For example, a retailer may be happy with a 20% open rate for an email campaign, but that still leaves 80% of your loyal opted-in customers who have not seen your offer.
What do you do with this 80%? The CRM team holds this data, and will be tempted to send another email, but that may not be the smartest approach. Maybe you should show them 5-8 banner ads over the next 5 days. Or change your bidding strategy on their search terms.
Or reconfigure their next landing page experience. A central repository of audience insights in a Cloud platform will allow you to target the missing 80% with a range of different tactics.
Making the right decision for the customer requires a disciplined and coordinated approach. At the centre of the operation you need an audience repository that can capture insights in one channel and activate them in other channels.
Better can be defined as targeting the next ad based on some form of new intelligence. The options to do this – identifying audience traits and deploying the fullest range of digital marketing tactics – is achieved faster, cheaper and better when connected to the Cloud.
Richard Wheaton is MD of 55 London
