How Analytics Can Optimise Your Retail Marketing Strategy
What Is Retail Analytics?
Let’s begin with defining what retail analytics means:
Retail analytics is using key data sets about your marketing, sales, inventory and other business functions to gain insight that can optimise business process. Using analytic methods retailers can draw conclusions from their data to answer critical questions about their business.
It will require a systematic process of collecting, storing, managing and visualising data. The data will most likely come from many siloed sources particularly as many retailers engage in multi-channel commerce.
So, the main objective for any marketing analytics strategy is to create a single view of the siloed data.
Bringing the data into a single view will help to better understand their business landscape by:
· Connecting the dots and making it easier to spot and identify trends.
· Compare performance across all sales channels, campaigns and platforms.
· Understand underlying causes of increases or decreases in KPI’s.
· Drill down quicker and more effectively to answers.
· Greater accuracy of data analysis and measurement.
· Complete picture of customer behaviour and journey.
· Generate insights that can more effectively personalise the customer experience.
Why Analytics Has Never Been So Important?
Timing has never been so much of the essence to ramp up your analytics strategy.
With so many more shoppers online, retailers have the opportunity to track customer touch-points.
Also, Retail Economics believe that the internet is expected to contribute to 53% of all retail sales in 10 years’ time. Their prediction was made prior to COVID-19 pandemic which has accelerated a wave of retail to shift to online.
With the ecommerce trend gathering pace, retailers need to make sure they are ‘analytics ready’ to capitalise on this channel.
The marketing and sales teams that are ready will have an opportunity to harness shopper insight that can optimise their strategy.
Analytic adoption in the retail sector can vary depending on sector and from company to company.
The main challenge that most retailers face is a lack of skills and expertise in their organisation. Particularly when moving beyond siloed data analysis on platforms such as social media, google analytics, email and online advertising.
But understanding how to unify these siloed data sources and generate accurate insights that impact your business is still a major challenge for retailers.
The shift to increasing focus on online sales and the resulting digital transformation required in retail companies both represents increasing challenge but also opportunity.
Key Marketing Analytics that Online Retailers Should be Adopting
Firstly, we need to understand marketing goals. The marketing goals should always be linked to specific outcomes.
· Decrease acquisition cost
· Increase conversion rate
· Increase average order value
· Decrease cart abandonment rate
· Increase Customer Lifetime Value (CLTV)
Once we understand our goals we know which are the most important KPI’s to track. But also, we will know which questions we need to ask of our data.
Below are example questions that any online retailer should be asking of their marketing and customer data:
· Who is most likely to Buy?
· When are my customers most likely to buy?
· Who are my best and worst performing customers and what do they look like?
· Which customers and segments have the highest and lowest average order value?
· Which customers and segments have the highest and lowest CLTV?
· Which customer respond best to which campaign?
· Which campaigns are generating best ROI?
· Which content and what type of content engages each customer segment?
These questions help to shape a picture of how retailers need to be developing their targeting, positioning and message.
It will also help where to spend their marketing budget and what proportion they should be allocating to each campaign or channel.
Taking all these considerations into account the retailer should be thinking about how their strategy can be tailored to each customer. By shaping a picture from the questions above it will help to personalise the customer experience but also understand the ROI associated to each individual customer.
The following are five analytic techniques that are key for any online retailer. They can be used to extract actionable and meaningful insight from their data:
Segmentation is categorising your customers by behaviour or by customer need. It relates to your marketing actions as different segments will require different marketing tactics.
Analytics is used to identify which segments exist within your business and how best to service their needs.
For example, you may have segments that are:
· Premium price buyers or low price buyers.
· Buy products individually or buy in bundles.
· Buy product in multiple categories or only in a single category.
· Only buy on promotional offers or on discounts
· Only buy through certain channels or buy on multiple channels.
Being able to identify which customers fall into which segment will help to improve your targeting. You will be able to tailor the message and offer depending on their buying habits.
The screenshot below shows an example of how you can cluster your customers by segment to quickly identify a) how each segment is performing b) drill down to each single customer in each segment
Example of a segmentation analysis using Alytix™ Customer Data Platform
2) Recency, Frequency and Monetary (RFM) Analysis
RFM is an important analysis as it is an indicator of key customer buying traits. It evaluates the time lag (recency) a customer has engaged with your business. It assesses how frequent (frequency) they visit your website or purchase a product. It determines how much the customer spends (monetary) on your products.
It is linked to other essential analytics and KPI’s listed in this article such as segmentation and retention and churn analysis.
RFM analysis will provide you with an overall picture of how engaged your customers are with your business. It will impact your marketing strategy as it will pinpoint whether you need to improve your retention or acquisition marketing.
It will also help to predict future outcomes. Such as which customers are most likely to become loyal customers or which products purchased lead to highest churn rates.
Again, the message can be tailored to each behaviour. Personalising the message will improve the outcome whether it be to retain, buy more frequently or increase their spend.