Table of Contents
- Introduction: Why Retailers Need Data Analytics to Succeed
- Obtain Comprehensive Customer Insights
- Optimise Omni Channel Merchandising
- Set Effective and Dynamic Pricing Strategies
- Enhance Product Placement Across All Sales Channels
- Optimise Assortment Planning with Clustering Techniques
- Streamline Inventory Distribution and Run an Efficient Supply Chain
- Guide Physical Store Openings and Closures in Covid-19 and Beyond
- Generate Personalised Product Recommendations
- Strategise Effective Targeted Marketing Campaigns
- Guard Against E-Commerce Security Threats
- Conclusion: Partnering with the Right Data Analytics Provider
Introduction: Why Retailers Need Data Analytics to Succeed
The transformation of the retail industry
The global retail industry has undergone a seismic shift in the past decade. From China to California, Singapore to Sydney, many hallmarks of the traditional shopping experience have been reshaped, relegated or replaced, as the rise of digital retail has compelled corporations to embrace new business models.
Can you remember the last time you schlepped all the way to a physical store when you could buy the exact same thing online with just a few clicks? Indeed, the once-ubiquitous shopping mall – crammed with customers patronising assorted brick-and-mortar shops – has been upended by e-commerce. Now, consumers have a wealth of online shopping channels at their fingertips: from marketplace behemoths like Amazon, Alibaba and Lazada to direct-to-consumer sales platforms.
This has fundamentally changed how the game is played. Cash is no longer king, and is often shunned in favour of digitised payment systems. Social media has transformed marketing tactics and sales initiatives. Customers are prioritising conscious consumerism, and want an increasingly personalised retail experience that cuts through the noise.
Clearly, businesses have their work cut out for them if they want to succeed in this new retail age.
Current and future challenges facing retailers
How can companies flourish in this big, bad world of hybrid retail? For starters, many organisations will have to make huge business changes to tackle these challenges head-on – such as putting digital transformation at the heart of their business models.
According to Mastercard’s Recovery Insights report, 2020 saw an additional US$900 billion poured into the global online retail industry. While Covid-19 lockdowns certainly affected this lofty figure, the study also predicts that between 20-30% of consumer spending that moved from brick-and-mortar to e-commerce will be permanent. Retailers have to run robust online operations to capture a share of that spending shift, or risk lagging behind.
As the online realm increases the number of sales portals, it will be crucial to ensure brand synergy across all platforms and provide a seamless omni channel experience. Furthermore, the shift to e-commerce has made it even harder for retailers to compete for shoppers’ attention as they demand increasing personalisation. Businesses must thus devise even more strategic marketing campaigns and deliver tailored experiences that drive sales.
Conscious consumers are also insisting on transparency. An IBM study revealed that 57% of respondents were willing to alter purchasing habits to reduce their negative environmental impact, while 71% would pay a premium for brands who provided traceability. Companies must hence prioritise sustainability if they want to retain and attract customers.
On top of these newer challenges are traditional problems that retailers have long faced: such as optimising assortment planning, determining effective pricing strategies and streamlining inventory. The Covid-19 pandemic has also added the inconvenience of opening and closing brick-and-mortar locations amid fluctuating lockdown restrictions.
Sounds overwhelming, doesn’t it? Thankfully, there’s a clear solution.
How data analytics can help address these issues
In order for companies to overcome these challenges, they can mobilise data analytics to ensure their present success and future-proof their operations.
Data analytics has become one of the most important pillars for retailers’ digital-transformation efforts. Put simply, it entails the collection, integration and processing of disparate data to attain granular insights. This statistical and qualitative approach removes guesswork, and empowers companies to make business-critical, revenue-optimising decisions with greater clarity and confidence.
Predictive analytics is one of the main data-driven strategies harnessed in retail analytics. By leveraging techniques from data mining, predictive modelling, AI and machine learning, it uses historic and present-day statistics – such as inventory levels, customer behaviour, seasonal fluctuations and other external figures – to generate forward-thinking and future-focused insights.
Heavy hitters in the industry such as Amazon, IKEA, Target, Sephora and Levi’s have all harnessed retail analytics to fuel their decision-making processes. And the sector is predicted to continue its exponential growth. According to an Allied Market Research report, the global big-data retail analytics market size is anticipated to reach a whopping US$25.56 billion by 2028, up from $4.85 billion in 2020.
For companies to be successful, they will need to incorporate these solutions sooner rather than later. In 2015, a Gartner report stated that retailers who did not adopt data analytics will find it hard to compete, as they will be unable to unlock certain revenue opportunities without the help of predictive analytics. And in 2021, Gartner identified data analytics as a core business function, and stated that mobilising this technology will allow businesses to be resilient in the face of change.
Indeed, to future-proof their operations, retailers must fully embrace the power of big data and integrate data analytics across their entire organisations now. Ready to get started? Check out our definitive guide to how data-driven solutions can addresses various pain points and allow your retail business to thrive for years to come.
Obtain Comprehensive Customer Insights
Think about all the data you glean through everyday customer interactions: such as their transactional history, limited demographic details and loyalty status. This should be a good place to start, no? But in order to generate in-depth customer insights that can be leveraged into making revenue-optimising choices, two things need to happen.
First, you need to go big or go home – by expanding and enhancing the scope of data you collect from both internal and external sources. You must then find ways to integrate, process and analyse these disparate data sets in a manner that offers comprehensive and prescriptive customer insights.
Through leveraging big data and retail analytics techniques, businesses can employ the right data-mining and data-processing methods to address these issues. Besides basic customer data, there are a myriad of other relevant statistics that retailers can tap into. These include figures that reveal customers’ online behaviour and buying habits; as well mobility data garnered from shoppers who access smartphone applications while within stores.
Once the raw data has been consolidated, it must be integrated and processed using the right software solutions to yield appropriate insights. Advanced predictive analytics software – which draws on techniques including machine-learning algorithms and AI capabilities – can help with analysing disparate data sets to generate comprehensive customer insights and forecast future buying behaviour.
For example, retailers can use data analytics to better understand customer characteristics in their immediate surroundings. The right tools will allow companies to garner granular data from customised catchment areas: including advanced consumer demographics, sociographics, psychographics, competitor stores, cross-shopping patterns, population density and in-store footfall and traffic levels. This data offers retailers crucial insights into their customers, and helps them determine what drives consumption in the immediate vicinity of their stores.
Armed with this important knowledge, retailers can proceed to make better, data-backed decisions that will keep their customers happy while optimising revenues.
Optimise Omni Channel Merchandising
In the nostalgic, old-fashioned days of traditional retail – when quaint mom-and-pop stores ruled the industry and cash registers had actual bells that “dinged” to log sales transactions – merchandising was restricted to a single channel: brick-and-mortar stores. Now, as retailers embrace a hybrid model and expand their e-commerce reach, it is critical to ensure synergy, integration and consistency across all touchpoints of the customer journey.
Be it a physical store, third-party marketplace, e-commerce website or mobile shopping app, companies must find a way to unifying their consumer channels. To do so, they will have to devise an effective omni channel approach as a central element of their overall business strategy. Thankfully, retailers can easily access highly specialised software, big-data analytics, IoT and other related technologies to optimise such efforts.
Through retail analytics, companies can collect a range of figures and build a holistic perspective of the processes involved in marketing, production and supply. By leveraging this technology, retailers can predict buying behaviour, formulate pricing and promotion strategies, optimise assortment planning for e-commerce and physical stores, streamline supply chains, react pre-emptively to upcoming trends and so much more.
Companies that have done so effectively include Apple, Starbucks and Levi’s. The latter in particular has adopted a unified view of its customers across various global sales channels, rather than see each region or platform as a siloed entity. By tapping into the possibilities of retail analytics, Levi’s has optimised its multi-channel operations, automated many key processes and built an efficient supply system to meet demand in its different markets.
The right omni channel approach can inherently improve operational efficiency and make your organisation more agile in the face of changing consumer habits. In adopting a bird’s-eye view, you can gain insights from multiple channels and manage them centrally. You can then create an overarching strategy that unites the disparate branches and merges the requirements of both online and offline customer bases into a seamless whole.
Set Effective and Dynamic Pricing Strategies
As the adage goes, it’s all about the money. Or, in this case, making sure that the price is right. Effective pricing strategies can make or break a business. Setting the right prices can lead to a glorious uptick in sales and revenues, while getting the numbers even marginally wrong can result in a disastrous outcome.
What factors do retailers need to consider when devising pricing strategies? These include core elements such as profit margins, competitor pricing, seasonal sales and inventory. E-commerce outfits further need to negotiate cross-channel pricing, dynamic-pricing initiatives and personalised deals.
By synthesising these variables, retail analytics can help businesses determine scientific and predictive pricing strategies that remove guesswork. These optimised pricing initiatives can then be rolled out across the respective sales platforms.
Take dynamic pricing strategies, which help e-commerce companies stay ahead of their competition and drive better profits. Predictive analytics tools are used to analyse aspects such as market patterns, competitor pricing, seasonality and overall demand. The software then provides insights on the maximum price that customers will pay for a product, and recommends a pricing model that allows retailers to remain competitive while simultaneously generating better profits.
Predictive analytics can be applied to pricing strategies across all channels. For example, a retailer in Japan faced declining sales in its brick-and-mortar stores. Using a simulation tool and sensitivity analyser that forecasted the effect of detailed inventory and discounting decisions on store performance, the retailer was able to craft a discount strategy that increased its revenues.
On the e-commerce front, a global clothing company employed data analytics to improve its online performance in China, which was lagging behind competitors. The company used a price-optimising application that analysed the price elasticity of inventory items and suggested figures for specific timeframes to increase future sales and profit margins. The result? A successful 10% increase in revenues, which encouraged the retailer to implement the software across other markets.
Enhance Product Placement Across All Sales Channels
Product placement is an important part of effective omni channel merchandising. Through optimising the arrangement of products, you can increase the sales of items that you have excess inventory of, that are nearing their sell-by date or that you simply want to push out.
Think of all the times you visited a store and left with a bunch of items that weren’t on your original shopping list. Chances are that they were placed at strategic positions to attract your attention.
For brick-and-mortar locations, factors that can affect sales include whether a product is placed at eye level; whether it’s hidden away in a dusty corner; and whether it’s part of a florid, attention-grabbing display at the store’s entrance. Of course, different rules apply for different items. For instance, customers will still make the trip to the back of the store for essential goods, but it’s unlikely that they will impulsively add an expensive item to their cart even if it’s placed next to the check-out counter.
To enhance the overall customer experience and maximise sales via effective product placement, retailers need data analytics. Data collected for this purpose includes video footage of customers within stores – this captures crucial behaviour patterns including how customers navigate the space and which areas they tend to dwell or cluster at. Predictive analytics software is then used to generate heat maps and trial and monitor different layout patterns to determine the ideal configuration.
The same analytics strategies can be applied to tackle the endless aisle of an e-commerce environment. As with physical stores, retailers need to know what products to present and to whom, and they must make sure that customers can find what they want. Hence, they need to devise a successful strategy to manage the endlessness of an online inventory.
By mobilising customer behaviour data and running it through predictive analytics software, retailers can devise evidence-backed strategies to determine the optimal e-commerce layout. This can include which items to showcase prominently on the landing page for particular customers; optimising search algorithms; and moving high-performing products into prominent positions.
Optimise Assortment Planning with Clustering Techniques
Assortment planning is a complex, high-priority process that’s tricky to get right. It can be frustrating and time-consuming to work out the ideal inventory that will optimise revenues, and hitting that sweet spot often proves elusive.
Furthermore, stores are dynamic entities driven by internal, external and surrounding factors – sales can be affected by everything from market trends to seasonal variations to whether it’s raining outside. Thankfully, analytics and clustering techniques can help to simplify and streamline the process and allow companies to make confident, effective and numbers-backed decisions.
How exactly does it work? First, the relevant figures – such as historic sales data, key consumer metrics, inventory levels, product categories, store space and product demand – are collected. The data is then processed through machine-learning and AI-powered algorithms that consider the impact of changing external factors on buying behaviour. Finally, the software recommends the right mix of products to maximise sales productivity.
For added efficiency, businesses can adopt clustering techniques to streamline and simplify the planning process. Effective clustering allows retailers to group together multiple sales channels that demonstrate similarities in customer behaviour. Targeting assortment-planning decisions based on these clusters can drastically improve sales margins and optimise inventory utilisation.
For instance, a retailer in mainland China wanted to grow its revenue per square metre across its brick-and-mortar stores. The retailer adopted an AI-powered retail analytics solution to cluster its over 300 locations based on similarity of customer preferences.
The optimal number of segments was chosen based on a silhouette score, which assessed the similarity of a store to its own cluster compared to others. Each segment was given a scorecard, which helped group the stores based on customer preferences such as preferred product categories and average spending power. This let the retailer apply customised segment-level execution strategies related to product assortment, thereby increasing overall sales productivity.
Streamline Inventory Distribution and Run an Efficient Supply Chain
Running an efficient supply chain is one of the top strategies for ensuring a cost-effective retail operation – and in order to achieve this, retailers must match supply and distribution with customer demand. In doing so, they can minimise wastage, streamline inventory and ensure that items are channelled to places where they will be snapped up by customers.
Predictive analytics is key to helping retailers optimise their supply and demand planning. AI and machine-learning technology can help with analysing demand figures and identifying patterns in sales, shipments and customer preferences. By pairing these numbers with external data, the software can forecast demand via predictive modelling and scenario-based planning.
Such tactics can give you a better grasp of supply and demand dynamics, which allows you to reduce inventory of low-demand products and prevent the depletion of high-demand product stocks. They help with anticipating future trends, so you can glean more accurate consumer-demand predictions. They also forecast the effect of upcoming periods of high demand – such as holiday or sales periods – which empowers you to improve the overall customer experience and maximise profits at a key moment.
Take the case of a global fruit supplier that needed to optimise its product distribution across different countries. It employed data analytics to predict the impact of Covid-19 recovery in various markets on consumer shopping habits. The software accounted for internal and external drivers, and used scenario-based planning to forecast different recovery scenarios and the resulting consumer demand. The company then allocated supply accordingly to maximise revenues.
Demand-forecasting models also have a huge impact at the manufacturing level. By using big-data tools such as AI and machine learning to accurately predict supply requirements, companies can eliminate excess production. Besides cost savings, this can greatly aid an organisation’s overall sustainability efforts – and who doesn’t want to help save the planet?
Guide Physical Store Openings and Closures in Covid-19 and Beyond
Ah, Covid-19. The bane of the global retail industry. While the supermarket sector was spared, growing a record 10% in 2020, total retail sales are expected to show a 9.6% contraction from 2019, largely due to lockdown measures.
As part of doing business in this punishing climate, retailers must juggle numerous challenges. Many countries are still undergoing sporadic lockdowns, which necessitates the sudden and intermittent closure of physical outlets. Businesses that can remain open may have to limit their opening hours or control the number of customers in their stores for safe-distancing purposes. Ongoing quarantines can create staffing shortages. And shoppers, overall, will have less disposable income. Sounds like a real headache, doesn’t it?
Thankfully, big data is here to help. To speed up their recovery, retailers can leverage data analytics to guide store openings. Through this, they can pinpoint ideal opportunities to capture value, minimise losses and optimise profits in this highly disrupted and uncertain market.
A key strategy to employ is impact analysis. This involves the collection of data such as Covid-19 transmission rates, population density, workforce availability, product trends, transactional data, customer demographics and data from markets that have successfully opened up again. The data is entered into a model and analysed to show how Covid-19 will affect sales during and after the restriction period.
Companies can complement these results with other granular insights gleaned from retail analytics – such as pricing and assortment optimisation, demand forecasting and customer behaviour – to make decisions about how best to open stores and identify key factors to accelerate recovery in their target markets. In doing so, they can ensure their continued success in the post-pandemic retail landscape.
Generate Personalised Product Recommendations
Run-of-the-mill promotions and blanket discounts? Pass. Be it boomers, millennials or Gen Z shoppers, today’s consumers are demanding highly tailored, increasingly unique retail experiences that cater to them as individuals, rather than making them feel just like any other customer out there.
Research by Accenture shows that 91% of consumers are more likely to patronise brands that recognise, remember and provide relevant offers and recommendations, while an Epsilon study uncovered that 80% of consumers are more likely to make a purchase when brands offer personalised experiences.
In order to capitalise on this market trend and ensure their future success, companies will need to possess the necessary tools and strategies to offer curated shopping experiences. But it can be challenging to gather, process and synthesise the relevant data in a way that generates conclusive insights. Retailers also need the right system in place to push out personalised product recommendations to their customers at scale.
How to get all of this done? Enter predictive analytics. The data-driven solution uses machine-learning techniques to process large amounts of data – including available inventory, customers’ browsing patterns and transactional history. Once the data is analysed, the software generates a set of curated and differentiated product offerings for each individual customer, thereby generating a tailored retail experience.
The product recommendations can then be delivered to customers across multiple channels: including the retailer’s website, through emails confirming a transaction, via a mobile app or as separate marketing blasts. This increases the probability to cross-sell and up-sell, which ultimately drives better revenues and helps with optimising profits.
Strategise Effective Targeted Marketing Campaigns
So you’ve determined your marketing budget for the year ahead. But how should you go about spending it? Why not draw on predictive analytics, which can help you target your marketing strategies in response to various scenarios.
While companies have traditionally used business-intelligence tools to guide their marketing activities, this technique is largely concerned with drawing on historic data to inform current efforts. Conversely, predictive analytics is a future-focused approach that zones in on harnessing micro-level data at the granular level of customers, transactions and vendors. The data is then processed through sophisticated algorithms that can yield more precise statistical insights, which are tailored to guiding future marketing initiatives.
For example, a global fruit distributor needed assistance with optimising its future marketing activities in the uncertain Covid-19 era. As its five major markets were located across the Asia-Pacific region, the retailer had to devise unique strategies that catered to the specific conditions of each locale. This required obtaining and analysing an immense amount of country-specific data – including spending across channels and total Covid-19 cases – to inform a forecasting model that would generate the relevant recommendations.
With the help of predictive analytics, the retailer was able to consider disparate data from internal and external origins to generate multiple scenario-based insights: these simulated different Covid-19 recovery situations and their associated implications on customer demand. The retailer’s marketing team could then apply the findings to guide country-specific marketing strategies.
Indeed, be it fresh fruit or high-waisted jeans, predictive analytics can empower you when it comes to spending your marketing dollars amid volatile consumer demand. Armed with data-backed insights that reduce decision-making risks, you can make confident choices about where to focus future marketing activities and design strategic campaigns to optimise profit margins.
Guard Against E-Commerce Security Threats
Forget stealth, dead-of-night burglaries and cash-skimming employees. The shift of retail from a predominantly brick-and-mortar operation to an online industry has introduced a host of arguably more sinister e-commerce security issues.
From credit card fraud, identity theft and fake returns to phishing, skimming, spam, bots, malware and hacker attacks, retailers need to successfully navigate these security hurdles in order to maintain customer trust and protect both their reputations and profit margins.
Card fraud is one of the biggest e-commerce security issues. According to a study by Nilson Report, global card fraud losses totalled roughly US$27.85 billion in 2018 alone. And this figure is only set to rise in the coming years. The report anticipates that losses will increase to US$35.67 billion in 2023 and notch a staggering US$40.63 billion in 2028.
Card Not Present (CNP) transactions represent a majority of card fraud cases. A report by Juniper Research revealed that such activity accounts for approximately 60-70% of all card fraud in many developed countries. The organisation also forecasts that retailers will lose about US$130 billion on fraudulent CNP transactions between 2018 and 2023.
So, what’s a retailer to do? To protect your operations against fraudulent CNP transactions, you can harness retail analytics as part of your overall fraud-prevention strategy. Predictive analytics, when combined with machine-learning tools, can analyse disparate data sets – such as customer behaviour, transactional history, browsing patterns and payment methods – to identify abnormal user activity and detect fraud.
By identifying, flagging and preventing any potential fraudulent activity before a customer completes a purchase, companies can halt unlawful transactions, decreases payment failure and increases sales conversions – thereby successfully safeguarding their business.
Conclusion: Partnering with the Right Data Analytics Provider
Ready to revolutionise your company with data analytics? As a retailer, you either already possess or have access to most of the information necessary to gain incredible insights into your customers, the wider marketplace and your own internal operations. You just need to unlock it by working with the right partner.
Teaming up with a proven and reliable data analytics provider can help you employ big data in an optimal manner. They will understand the unique challenges you face and be equipped to offer a tailor-made retail analytics solution from their wide arsenal of cutting-edge software and technologies. They can ensure that accurate and relevant data is collected and processed efficiently and effectively, while adhering to data protection laws. They will also have a wealth of expertise garnered through years of industry experience.
Founded in 2010 and headquartered in Singapore, Lynx Analytics is the ideal partner to help you harness data analytics to future-proof your retail business. Lynx Analytics is a leader in AI and data science solutions, with a strong expertise in predictive analytics models that incorporate the latest AI and machine-learning techniques.
Through its range of both off-the-shelf software products and consulting services, Lynx Analytics empowers global retail companies to generate better data-driven customer insights and predict future trends. Its insights are unique and highly targeted to each individual client, and can be put into action immediately.
Contact the team to find out more about how Lynx Analytics can power your retail business into the future.