In retail: From data deluge to data insight

  • From searching to purchasing, transactions constantly generate data
  • This data deluge could swamp retailers – or buoy them

In retail: From data deluge to data insightTHE retail industry in Asia has enjoyed healthy growth and remains a destination of choice for global retail chains.
 
However, retailers are facing greater competition, higher operational costs, and other challenges like slowing regional economies, high inflation and interest rates, and even changes in governmental policies.
 
On top of all this, retailers have to cope with understanding the demands of their customers – for instance, knowing when and why they choose to purchase certain products at certain times are essential for retailers to build their business by organising resources, appropriating funds, and managing inventory.
 
As a case in point, take the recently implemented Goods and Services Tax (GST) system which has come into effect this month in Malaysia.
 
Malaysian businesses now have to cope with understanding the intricacies of this new system, how it affects their customers’ purchasing habits, and ultimately, how it affects their bottom line.
 
In fact, Hong Leong Investment Bank Research has noted that on top of being GST-compliant, retailers have to cope with a lower consumer spending sentiment – GST and other causal factors have contributed to the decline in Consumer Sentiment Index and Business Condition Index to below the 100-point threshold, to 98 points and 95.9 points, respectively.
 
So how do retailers address this situation, and where do they begin? This is where big data analysis comes into play.
 
Retailers may not always know what their customers’ needs and sentiments are, but what they have is data … lots of it – in fact, they’re drowning in it. According to Forbes, data worldwide is growing 40% per year.
 
In retail: From data deluge to data insightFrom searching to purchasing, transactions constantly generate data. This data deluge could swamp retailers – or buoy them.
 
A recent IDC study commissioned by Microsoft forecasts that retailers worldwide could gain US$94 billion in value over the next four years by improving their synthesis, analysis and use of the data they collect (click infographic to enlarge).
 
Historically, retailers needed a data scientist using specialised tools to analyse significant amounts of business data, creating jargon-heavy reports. But with familiar business software that most retailers are already using today, along with affordable cloud computing, retailers can readily start small to plumb their data for better insights.
 
In fact, a McKinsey analysis of more than 250 engagements over five years has revealed that companies that put data at the centre of the marketing and sales decisions improve their marketing return on investment by 15% to 20%.
 
That equates to a whopping US$150 to US$200 billion of additional value, based on a global annual marketing spend of about US$1 trillion.
 
With the easy-to-use business analytic tools available today, virtually all store managers can import, drag and drop data to analyse their displays, stock management, customer engagement and more.
 
Smaller projects though, are quicker to execute than larger initiatives, so retailers can see immediate results – and dividends.
 
There are several ways retailers can leverage existing data and tools:

  • Cross the streams: Start by identifying your data silos – such as sales category data, supply data or campaign results – and finding ways to bring them together. How do specific product line sales compare across locations? How do loyalty card sign-ups correlate with pre-orders or returns? Assessing your data helps you formulate smarter questions about your business.
  • Zoom in: Broad demographic segmentation is giving way to micro-segmentation – using more factors to create richer, more individually detailed target groups. Instead of a vast category of women 18 to 35, micro-segmentation can give you women, 25 to 29, who live in specific postal codes, listen to K-Pop, shop five times per month, and drive one-third of sales for your top product. Such detail helps businesses personalise offers and service to increase sales.
  • Go public: Supplement your proprietary data with public data, much of which is available for free in formats that can be imported directly into spreadsheets or other applications.

For example, weather data from the Malaysian Meteorological Department, supplemented by data on daily sales, can reveal how weather influences shopper behaviour. Census data shows which customer segments are growing fastest in different parts of the country.
 
And data from social media sites like Twitter and Facebook can reveal customer sentiment and give early insight into trends – especially for new technology or product categories for which your past sales data simply could not offer guidance.
 
An example of big data in action in retail is restaurant chain Blackball, which specialises in Taiwanese tea and desserts.

In retail: From data deluge to data insight

Blackball (pic, above), which has more than 40 outlets in Malaysia alone, uses perishable components in its desserts, so it is critically important to get ingredients to the right place at the right time.
 
By adopting a hybrid cloud solution, it was able to integrate sales data with regional weather patterns and social media feedback, allowing it to manage its stock more efficiently and better serve its customers.
 
As a result, BlackBall was able to make business sense of unstructured data and was able to enhance its products and execute more effective promotions, without investing in additional on-premises infrastructure.
 
Retailers can tread water in the data deluge, or dive in to create data dividends. By starting small, with existing data and business tools, retailers can channel a productive data stream that brings insight, informed decision-making and ultimately, profit.
 
Dr Dzaharudin Mansor is the national technology officer at Microsoft Malaysia.
 
Previous Instalment: The billion-dollar opportunity for ‘data smart’ manufacturers
 
 
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