The amount of data being collected and the ability to analyse it and provide more targeted customer propositions and better understand the drivers of value are key trends reshaping the future of retail.
Retailers need to understand how to analyse the data collected to get a better understanding of their customers. This report looks in depth at what customers value, how they think about data and how retailers can use data to better manage customer engagement and pricing.
While price might still be important, customers today also value convenience, speed of delivery and personalisation. Retailers need to know which factors different customers value the most and how to deliver them that value.
Price is still key because it is more transparent, especially now that consumers can shop anywhere, anytime. Price transparency remains a critical element of the value mix, but needs to be considered alongside other values including customer satisfaction – now more measurable than ever – and delivery strategy for online purchases.
This report is a review of the realities of the challenges for modern retailers. As the amount of data being collected grows, we will also look at how retailers can ensure privacy and meet customer concerns while still being able to utilise useful customer information. While the solutions will be different for every retailer, this report sets out the landscape and provides some inspirational examples of brands that have effectively begun to utilise data insights to improve their business outcomes.
What do customers really value?
Empowered by technology, today’s connected consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but now control and experience are also important. On the other side of the equation, retailers have access to more insights than ever before, empowering them to get a deeper understanding of what drives their relationships with their customers.
Accenture Consulting has a useful definition of consumer value as being the sum of tangible and intangible benefits (e.g. emotional reward) the consumer receives from making a purchase, relative to what is given up to make the purchase.
Looking at the big picture, Accenture believes the balance of power is tilted in favour of consumers and that industry will capture only 32% of total value at stake, primarily from through migrating from brick-and-mortar to e-commerce. Consumers will benefit by capturing 68% of total value at stake, which represents consumers’ cost and time savings.
The upshot is the consumer will greatly benefit from digitally-enabled business models, and industry players need to focus on capturing the value migrating to these new models.
According to the Boston Consulting Group (BCG), retailers must understand what the customer values and is willing to pay for and, ideally, how the retailer can differentiate itself and build loyalty.
BCG suggests it’s important to avoid defining customer value too broadly or superficially. The definition must be sufficiently granular to be actionable. They point out that many retailers say that their customers prize “value” or “a high degree of choice”, but they note these concepts are too broad and vague to translate easily into concrete action.
BCG also sees a further level of complexity for retailers: consumer value differs between customers and the numerous technologies, channels and business models developing across the industry may make it even harder to identify a specific consumer’s value equation. It highlights that it may not be enough for retailers to take action on data; they need to find the right course of action on data and this may involve a process of continual adjustment and improvement.
To meet expectations and delight the consumers of the future, retailers must keep up with and shape all the different dimensions that contribute to consumer value appropriately.
The good news is that retailers entering the journey now can avoid some of the mistakes already made. Consultants Bain and Company note retail economics have been challenging, and in hindsight some retailers have made some steps that aren’t worth replicating — but they have also created new customer propositions while striving to manage pricing and face some big questions, such as what you can charge for and what you need to give away.
For example, in the battle for market share, some European grocers have neglected to charge for the extra effort required for delivery, exposing themselves to financial trouble. Now, consumers have been educated that picking, packing and delivery come at no cost to them. That’s difficult to reverse.
The lesson to retailers making those new decisions is clear: it’s vital to look for ways to educate the market from the start on the price and value of the service you are providing.
The data is there – now to build the insights
Accenture notes barriers to entry have fallen as visibility and brand presence no longer depend on expensive television advertising. Rapid growth in the number of new business models makes it much easier for companies to micro-target offerings that meet precise consumer needs. Instead of employing a discrete business model, industry players will operate flexibly across a portfolio of models. No company will be able to do everything in-house.
“There is no shortage of information, no shortage of data. Retailers are long on data, long on information, but short on insight,” says Jerry Macey, National Retail Lead at the Commonwealth Bank.
“The key is using the information that’s already in your business. It’s not about adding more to it, there’s plenty of that. It’s about using the data, and the information that you already have. It’s having your various platforms across the business from finance to customer service integrated and talking to each other.”
Scott Rigby, Head of Digital Transformation for Enterprise Solutions at Adobe APAC, notes that the recent Adobe Digital Trends in Retail report shows that pulling actionable insights is, for much of the industry, still an aspiration. Across the sector, “improving data analysis capabilities” has become the top internal priority.
The disruptive breakthroughs of companies like Amazon and Alibaba in achieving rapid revenue growth and capturing market share have highlighted the importance of leveraging data science as a core capability throughout the organisation to drive decision-making, yet it has not been adopted at the rapid rate that the industry requires.
Analytics is becoming ever more sophisticated and its use within organsations is proliferating, which makes the speed of capability development crucial for retailers. Advanced analytics drive profits because they provide real-time responses to market shifts and can better inform innovation initiatives.
The primary obstacles preventing retailers from speedily developing these capabilities include the expense of developing an insights-driven organisation, a lack of data scientists in the workforce and infrastructure gaps that prevent action on the insights generated by predictive analytics.
Moreover, consumer relationships are difficult to secure and maintain in a world of increased competition. This makes it imperative for predictive analytics to be applied throughout a retailer’s end-to-end value chain and that this capability is continually upgraded to keep pace with industry demands.
Real-time data will underpin just-in-time models in supply chains, which can potentially lead to major efficiencies in the availability of materials, delivery schedules, manufacturing capacity and staffing considerations.
As changing external factors (like fashions) shape internal decisions, it will be imperative to stay ahead by making “smarter” decisions. This will require critical data to be shared easily among manufacturing teams, from the factory floor to the executive suite.
The consumer baseline
There is a baseline expectation that consumers now have when they hand over their data to a company. Research from the Kellogg School at Northwestern University asks us to consider that today many customer-service hotlines—whatever the business—tend to keep a record of their customers’ personal information, past transactions and preferences as a way to save time and make interactions more efficient.
“Now, when customers give their information to a company, they expect everyone from that company to have that information,” Kellogg Professor Michal Maimaran says. “Anything less falls short of the standard.” Service models that feel burdensome or redundant are not well tolerated.
EY suggests that most consumers don’t see much value in their data. They’ll willingly accept terms & conditions that allow a company to monetise it, if that gives them “free” access to a fun app or a useful platform. But the key question is if and when that might change.
Consumers are becoming more reluctant to share anything other than basic personal data, largely due to privacy worries. What if that reluctance developed into a deeper shift in attitudes, and consumers started demanding better terms or more value from companies that monetise their data?
EY suggests this could be an opportunity. Companies could respond to this shift in consumer sentiment by offering better services and bespoke data use terms, all supported by greater transparency between themselves and their customers.
As people recognise and understand the financial value of their data, they could make different choices about what they share and with whom. T&Cs could become more fluid and negotiated by artificial intelligence.
Then again, if people who choose not to share their data had to pay for a service that others get for free, perhaps opting out will become a choice only the relatively well-off can afford.
“People will happily share things about themselves as long as there is a benefit to them and as long as you are responsible with that information,” Bryan Gildenberg, chief knowledge officer at Kantar Retail in Manhattan says.
He also suggests that in the future, personal data could build customer leverage. “Instead of bargaining on price, the now twenty-something shoppers will be bargaining for a better return on their data — essentially demanding more privileges and perks that money can’t buy in exchange for handing over vital statistics.”
This view is backed by data from Deloitte quoted by internet analyst Mary Meeker showing that almost 80% of US consumers are willing to share personal data if there is something in it for them.
Data’s role in retail decision-making will grow, especially as technologies like Big Data and machine learning continue to mature. Forward-thinking retailers will keep exploring ways to collect and leverage data in their sales, marketing, customer service and operations.
Binoo Joseph, Head of Technology at retailing giant Tesco PLC, based in Singapore, says that retailers need to be able to use data to:
- Provide customer specific offers and discounts
- Give store assistants access to customer information and preferences
- Provide sales suggestions based on purchasing history
- Deliver personalised loyalty awards in store
Advantages in the brick and mortar world
When a grocery chain expands its international foods section or a pet supplies store moves dog treats closer to the front door, it’s not by chance. Retailers are taking advantage of assets that their e-commerce competitors lack—physical space and the organisation of products within that space—to enhance customers’ shopping experience and boost sales. Increasingly, they are using big data to do it.
At a time when customers can find almost anything they want online, brick-and-mortar stores must make it easier for shoppers to find the right products in the right place at the right time. Deeper, more powerful data analytics can help improve store design, floor plans, merchandise mix and other elements of localisation.
To succeed, retailers must apply data ¬analysis to more aspects of localisation. They must use it more consistently and apply more sophisticated algorithms. Those algorithms should connect floor space, assortment and product placement with loyalty, web browsing and store heat map data.
Privacy and security
Data privacy will be critical to maintaining trust. Given the increasing role that digital plays throughout the retail industry, there is already a growing threat relating to cybersecurity that could have extremely detrimental implications for retailers and consumers alike. Data security will be essential to prevent situations that could compromise consumer relationships.
There is also a significant opportunity associated with consumer data. Accenture notes that recent reports have framed data as the “new oil” – the most valuable resource in the world.
They suggest this idea should be expanded to include data as the “new soil” – the basis on which consumer and market value can grow.
Given that the fundamental driver towards future operating models is a reorganisation to address consumer needs, copious amounts of personal data are required to establish consumer intimacy.
This ongoing data collection gives rise to concerns about the privacy and security of personal information and how companies are using it.
Recent surveys find that 57% of consumers are concerned with how businesses use their information, with 41% of consumers stating they need companies to have greater transparency if they are to have confidence in their products/services.
Consumers want both companies and the government involved, but say companies bear a larger share of the responsibility.
Companies should do more than what’s required by law—proactively managing cybersecurity and privacy risks in a way that goes beyond a compliance “checklist” approach.
Consumers also expect more from regulators when it comes to protecting data. As the frequency and scale of data breaches continue to rise, so do consumer worries. Companies must understand and address these concerns or risk losing business.
PwC suggests that companies can make their job easier by targeting their messages using the enormous amount of consumer behaviour data now available, but they need to tread carefully. While 41% of respondents to a PwC survey are comfortable with retailers monitoring their shopping patterns to tailor special offers for them, just 34% want retailers sending special offers when they are in the vicinity of the store. Don’t start sentences with a numeral. Furthermore, 37% of global respondents are opposed outright to location-based offers.
In Australia, a Consumer Policy Research Centre (CPRC) survey released in May 2018 indicated that while some consumers (27%) found the use of their data for delivery of customised content to be beneficial, the majority (52%) believed the monitoring of online customer behaviour for use in targeted advertising to be “unacceptable”.
“The older consumers are very concerned about data privacy and hence trust in the companies they buy from and share information which is important. On the other hand, younger consumers are less concerned about privacy. As long as they can gain a benefit from sharing their information, they will do it. This trait will become mainstream in the future.”
— Stephan Fetsch, Head of Retail, KPMG in Germany
A model for market insights from public data
Euromonitor International suggests that retailers could improve their customer knowledge and target their offerings better by using data analytics. For example, analysis of income and wealth will help to determine the best locations for various offers. Are there enough high net worth customers in an area to justify a high-end retail offer? Or, is the customer data suggesting a more cost-conscious demographic?
Euromonitor give the example of an analysis of income distribution by city to help answer critical business questions such as:
- What is the size of the income target group and, therefore, product potential in our operating cities?
- How is our target group evolving over time, is it shrinking or expanding?
- What are the nearest attractive cities for expansion based on population purchasing power?
- Are there enough households to support sales of a new product?
- Should we follow a premium or mass strategy?
- How will the impact of macroeconomic shocks on our target group vary across the cities we operate in?
They also give the example of using data to size the future potential of a market from a demographics perspective. Businesses can identify which age segments are growing and declining or breakdown by gender.
In Australia, the Australian Bureau of Statistics publishes detailed suburb-by-suburb data on income distribution in its Household Income and Wealth series. In 2018, it also published a breakdown of income distribution across Australia’s council areas (the Estimates of Personal Income for Small Areas data) spanning the period between 2011 and 2016.
And increasingly there are business services that can provide detailed data to add a layer of rich insights from actual spending. The Commonwealth Bank’s Daily IQ platform, for example, gives its business customers insights into customer spending patterns broken down by age, gender and location, as well as data on changes in consumer spending by postcode, suburb, and local government area across Australia.
EY states that most of the purchases we make today require some element of conscious choice but as AI bots, concierge services and smart-home systems become more intelligent, they’ll complete many of these transactions for us.
Consumers will receive competitively priced and relevant products via technology-based platforms that serve as access points to new buying ecosystems. These platforms will use AI and machine-learning technologies to draw on vast quantities of data and predict or preempt customer behavior. This will disintermediate transactions, payments, fulfillment and brands.
Using analytics to target the consumer
CASE STUDY – A wearable device that helps lift purchases
“Over the course of the last five years we have been on a digital transformation journey, which started with sponsoring from our board around the need to invest in that space.
Five years ago when we started this digital transformation journey, I was the only body that we had in the business working on it, and today we now have in excess of 40 people in our business who are principally responsible or who are part of my team that enable transformation.”
– Mark Teperson, Chief Digital Officer, Accent Retail Group
One interesting example of analytics is the introduction by Australian retailer The Athlete’s Foot of “MyFit Pod” which is a tracking device attached to running shoes. This device provides benefits to the customer by providing data such as how many kilometres they have run. It also brings benefits for the retailer by giving them insight on usage and therefore wear and tear and when to target offers for replacement shoes.
Mark Teperson of Accent Group (the corporation behind The Athlete’s Foot) described it as follows: “We have a pod, a device that we sell, that we put on their running shoe, and that actually tracks and measures the kilometres that you’re running. It gives the consumer feedback in terms of their run efficiency scores and their cadence. It gives them tips on how to run better.
“It’s providing The Athlete’s Foot with actual running telemetry data, so that we know when we next expect to see them in-store and we can target communications and tailor communications based on where they are in the useful life of their product. We’re starting to notice we’re having an impact on our purchase cycle, where we’ve seen a reduction in the purchase cycle, the number of days between customer visits, of up to 12%.”
AI that shops for you
EY notes that speed, platform and trust will always matter. But scale needs to be defined differently.
In the future they suggest we’ll increasingly trust AI to complete many of life’s more ordinary purchases, especially for the consumer products we use every day. Bots will curate choices for us and buy on our behalf, if we give them that permission and access to our data. It will evaluate what products or services we need, when we need them and where best to buy them – not just in terms of finding the right price, but also in terms of sourcing brands and suppliers that align with our values.
The more relationships in the AI ecosystem, the more transactions processed, the more data gathered and analysed, the smarter these platforms will become.
Pricing will be dynamic and customised as AI analyses demand and supply in real time. The best example of this is Uber, which already uses technology to institute surge pricing during periods of high demand.
Personalisation and the science of pricing
Product recommendation systems driven by big data analytics were transformative for the retail industry. Due to the use of huge databases that could track user shopping patterns, developers were able to introduce product recommenders. The product recommenders are “you may also like”, or “inspired by your browsing history” as seen on Amazon.
It can also dramatically reduce marketing costs. For example, Netflix has saved millions thanks to in-product promotion of content. Its data analytics and AI algorithms target users with personal show recommendations so accurately that Netflix doesn’t need to promote its shows outside its own platform.
CASE STUDY: Vineyard Vines and email marketing
The Harvard Business Review reported how US clothing retailer Vineyard Vines had success with more personalised and targeted emails. They implemented an AI-driven system that takes data about the customers’ interactions with specific products and decides what products to target to each customer next, based on its understanding of the individual and the collective wisdom of all customers.
The algorithm understands:
- which customers have price sensitivities (and therefore are motivated by discounts);
- what items an individual customer viewed;
- what those items have in common with other items the customer engaged with;
- which products are replenishable items and at what time a specific customer replenishes them;
- which activities predict a next sell;
- the right timing to contact a specific customer;
- a customer’s lifetime value and activity, and so on.
The company used this technology for its annual Easter campaign. The number of email recipients jumped from 5,000 to 150,000, open rates were up 77%, and revenue per email was up 759% over the prior year’s results.
The Vineyard Vines story is a great example of how data and AI need not be the preserve of very large retailers. It shows how retailers of all sizes can benefit from getting data to work for them – and sometimes, that’s just about doing some basics right.
Tesco’s Binoo Joseph pointed out at a recent conference that when you walk into a store today most retailers don’t know if you’re an existing customer. Joseph noted that customers today expect hyper-personalised service across both physical and online channels and that customers expect their experience to automatically adapt whether they engage physically or digitally.
As Macey explains, the challenge for retailers is that “the data needs to solve a problem for their customers. Make it helpful, so that above all, it creates a good customer experience”.
Customers should feel like their retailer cares about them and understands them. The retailers that manage this will have a distinct edge in a competitive world.
Big Data and loyalty programs
Retailers can also use big data to draw customers to their doors. Loyalty programs and personalised service are becoming more important in the quest to develop customer relationships. They will be further enabled by technology. Today’s electronic measurement tools allow retailers to obtain a 360-degree view of customers’ habits, creating sales marketing segmentation based on purchasing information from all available channels. By tracking a customer’s every move and penny spent, retailers can offer reasons to come back: special offers, perks, discounts and other opportunities.
Guideshops is a concept introduced by US menswear retailer Bonobos that has all the advantages of a high-end, high-touch retail store offering the opportunity to deliver strong customer relationships. Customers can try on clothing, get plenty of advice — and be enticed with possible accessories and add-ons. At the end of the sales appointment, the goods are ordered online and shipped to the customer at home. Since customer details (such as sizing and favourite styles) are recorded in the Bonobos data system, customers are more likely to make online purchases unassisted in the future, driving customer loyalty and lower levels of product returns, even for e-commerce transactions.
Amazon and a note of caution on data vs customer anecdote
Amazon CEO Jeff Bezos has an interesting take on keeping up with customer views, he has an email address where customers can contact him directly. He views that email address, [email protected], as a way to stay close to customers, which can otherwise be hard to do as an executive who is far removed from day-to-day customer service and sees the company mostly through data and reports.
“We have tons of metrics,” Bezos explained. “When you are shipping billions of packages a year, you need good data and metrics: are you delivering on time? Delivering on time to every city? To apartment complexes? … Whether the packages have too much air in them, wasteful packaging.”
So those customer complaints give him front-line insights. If all his data say one thing and a few customers say something else, he believes the customers.
“The thing I have noticed is when the anecdotes and the data disagree, the anecdotes are usually right. There’s something wrong with the way you are measuring it,” he explained.
This is one of the ways Bezos expresses what he calls one of Amazon’s most important values: customer obsession.
Bringing science to pricing
Consultants KPMG have looked at a scientific approach to pricing. Price matching or promotional activity are tactics used by many retailers to stimulate customer engagement. However, KPMG suggests that as consumers become increasingly desensitised to continuous promotion cycles, the fight for each dollar becomes tougher and margins get tighter. They suggest that a better solution is smarter pricing or a process called ‘retail price optimisation’. To bring retail price optimisation techniques to the Australian market, KPMG is working with global price optimisation software firm Revionics.
Retail price optimisation analyses how customers respond to different prices in retail channels and then identifies the prices that will best meet a retailer’s aims – higher profit, for instance. It does this with a mathematical model that adds science to pricing – crunching data on prices, sales, inventories and other important factors. For many, retail is still an art, not a science, despite the increasing complexity of their environment due to the impact of technology.
Jonathan Attia, of KPMG’s Strategy Group, notes that continuous promotions will reduce customers’ propensity to pay when items are not discounted, as they wait for a promotion. This is something that has been driven home to some of Australia’s major retailers in recent years as they have tried to end the frequent cycles of discounting.
Simply “investing” in price discounts through frequent promotions across whole ranges is too unsophisticated in today’s environment when competitors are using much more scientific means to drive specific results for specific items. Retailers need to use data insights to understand what type of promotions, for which products and over what timeframe, will optimise results.
“The science can enable fundamental decisions at the product level in terms of trading off between enhancing your revenue or enhancing your margin dollars,” Attia says.
Revionics estimates that determining appropriate prices in this way can provide a business with gross margin improvements of between 2 to 20%.
Revionics’ chief marketing and strategy officer Cheryl Sullivan estimates that more sophisticated pricing can bring the following improvements:
Sullivan says the optimisation process can then provide recommendations on the optimal mix of prices to support different retail strategies — for instance, increasing traffic, growing basket size or enhancing margins. It can be used to find specific tactics for promotions, such as whether an item should be on the front page of a catalogue. It can also tell you when you should shift from driving volume through lower prices to driving margin through higher prices. She says it can also tell you when a promotional offer “shouldn’t be a 25% off, it should be a 10% off”.
Thanks to the internet and mobile devices, companies are already collecting large amounts of data and using those to personalise advertisements they offer customers. Pricing is a next step, says Sanjog Misra, professor of marketing at Chicago Booth School of Business. “Personalisation of these things is already starting to happen,” he says. And while this raises concern among privacy and consumer advocates, he says people may come around to his view, that “information-based pricing or advertising is the right way to go.”
If a seller could charge all customers the highest amount they are willing to pay, it would maximise profits while providing the customers with the products they want or need. This was an advantage of haggling, as individualised prices theoretically benefited both businesses and customers.
Even in the age of the price tag, businesses have sought to balance profits with supply and demand. Airlines, for example, adjust prices based on variables, and two people sitting side by side on a flight may have paid very different prices depending in part on when they purchased their tickets. From there, it’s not a big leap to move toward more customisation.
Airlines may use a rewards program or the days and times a person flies to determine whether she is traveling for business. If she comes back on a Friday rather than staying the weekend, for example, she’s probably flying on business and on an expense account. The airline might charge a little more.
An airline doesn’t have to know much about a person to do that. Imagine what it could charge if it also factored in location, income, credit score, number of dependents or other available information. Many of these data points could be used to determine how much people will pay for given goods and services.
Perhaps the biggest drawback of individualised prices is that they could offend customers’ sense of fairness, because targeted pricing is a degree of price discrimination. While the word discrimination may sound sinister and prejudicial, both second- and third-degree price discrimination are actually fairly commonplace. Sliding prices based on quantity constitutes second-degree price discrimination—for example, a buy-two-get-one-free deal, or a company incrementally charging less for each product as more of the products are purchased. Third-degree price discrimination occurs when customers are segmented and charged different prices, such as when senior citizens receive discounts or when prices differ by location. How retailers manage to strike this balance is a fascinating challenge for the retail industry.
Case study: Alibaba’s use of data
Boston Consulting (BCG) have noted that the use of data and analytics are crucial to both Amazon and Alibaba, but they are used in different ways. Amazon uses data primarily to refine its product and service offerings on the basis of consumer buying patterns. The company also shares data with merchants to help them list the right products, price competitively and manage inventory. Alibaba provides a broad data set on consumer behaviour that enables merchants to improve their marketing ROI and increase the conversion rate on their digital storefronts. For example, the data might reveal that a merchant’s highest-value customers visit after work—so a campaign might have a greater impact in the evening than in the daytime.
Alibaba can provide these powerful analytics because of the rich data it draws from its large ecosystem. As consumers move seamlessly through its various sites, Alibaba collects information on their shopping habits, digital media consumption, logistics needs, payment and credit history, search preferences, social networks and internet interests to better understand their behaviours and needs—using a “unified ID” to link consumer data across different sites. Drawing on its detailed data on nearly 500 million monthly active users, Alibaba has identified 8,000 different consumer descriptors, so that merchants can home in on their target customers with extraordinary precision—and increase the effectiveness of their consumer engagement efforts.
Alibaba also uses this data to provide a truly personalised shopping experience for the consumer, to a degree not yet seen in the West. While Amazon offers product suggestions based on a consumer’s searches or buying history, Alibaba may suggest new brands, promotions or content that consumers didn’t even know existed. Those suggestions tend to be spot on, driving exceptionally high click and follow-through rates.
Alibaba is increasingly looking to provide retailers technology services for their online and physical stores. Its first Australian customer is The Iconic, which is using an image search tool built by Alibaba, which allows shoppers to take a picture of an item of clothing they like and then displays similar products. For example, the computer recognises an image of a checked shirt and will show similar products available for sale on The Iconic website.
The intelligence ecosystem is moving beyond digital platforms and into physical retail, with Alibaba introducing smart tags at physical retailers in China. These tags on the shelves in retail outlets can be scanned with a mobile device to get more information on a product (for food or health items this could include the providence and origin). The tags also allow customers to order directly from their mobile device and have the product delivered. Connecting this back seamlessly to the shopper’s digital account, where it can be integrated with their existing data set, is a powerful channel for marrying people’s browsing behaviour with their in-store activity.
For the retailer, the smart tags allow dynamic pricing and the potential to offer strategic discounting. They ensure consistency and do away with the need for staff to walk around changing price labels.
Strategies for success
A hallmark of operating models of the future will be their agility. A survey by Accenture found that only 15% of industry leaders believe their operating models can respond sufficiently quickly to changing market conditions, while more than 62% of digital disruptors believe their operating models can do so. Organisations will need to continually evaluate the external environment and nimbly adjust to changing conditions. They will be self-organising, and their work will be project-based, with new initiatives forming organically. Teams will act with resolve and resiliency, prioritising progress over perfection – a “corporate garage” model enabling rapid experimentation and the development of “minimum viable products” that they test quickly, learning from successes and failures. Teams will also respond to changing market dynamics and innovate at a rapid pace, having the will to experiment and the ability to do so. The end goal will be value creation across all stakeholders.
PwC suggests that retailers use customer demand and insights to reduce their range by one-quarter then negotiate better pricing for the remaining products. Differentiating your brand and being focused is key. Providing an all-encompassing brand, they suggest, is business suicide unless you are a leading global retailer. Customers expect you to curate, so applying a ‘good, better, best’ strategy is vital to reducing costs while still satisfying customers. Cutting your range by 25% will streamline your inventory while also reducing in-store stocking, picking and ticketing costs, resulting in improvements in inventory and labour productivity. To be sure you are cutting back in the right areas, it’s critical to use customer data to fine-tune the offering so it is consistent with the demands of your core and emerging customers.
The future of retail will be built on insights derived from proprietary data – in particular, consumer data. Businesses must act now to reap the rewards of the consumer data gold rush by moving from simply collecting consumer data to using it to support, scale and systemise better decision-making.
More data is not needed, rather companies need to be able to analyse the data they already have and use it to provide useful customer insights.
At the same time they have to be aware of privacy concerns and ensure they are able to protect the data customers have shared with them.
As Macey said to BI Research: “It’s not the data, it’s the insight. That comes from the information that you currently have. It’s not about new data or overlay. It’s the insight that you can drive from your existing platform to bring focus and purpose to the decisions that you make at your executive table. Be that about product, be that about sourcing, supply chain, store format, customer experience, or fulfillment. They all tie together.”
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