The clash between retail and e-commerce is real. Does this mean we are nearing the end of brick-and-mortar? Not necessarily so, as physical stores offer benefits that remain untapped for online retailers. As the recent Deloitte retail industry report suggests, the convergence of retail and technology is one of the pivots of the industry’s disruption.
In this context, to win over consumers’ hearts, retailers need to turn to business intelligence consulting in order to adopt truly data-first ecommerce strategies.
Successful retail can only be omnichannel
Long gone are the days of provider-centric retail where customers were tied to a limited number of local stores and remained loyal to them because of convenience and the lack of choice. Today, in the customer-oriented world, running a successful business is impossible without using varied channels to reach and engage consumers. This is also one of the reasons why virtual reality retail has gained significant traction in the past few years, and the adoption of MACH architecture is becoming more popular and widespread.
By expanding beyond in-store-only services and offering multiple channels for customer engagement, modern retailers can provide consumers with the flexibility to choose the most convenient platform at any given moment. In return, retailers gain additional opportunities to connect with diverse audiences at various touchpoints and attract more buyers going forward.
Big data is an enabler for an omnichannel strategy, as it delivers rich, detailed insights into various aspects of retail that allow for an integrated cross-channel experience. However, before retail brands can capitalize on business intelligence, they should understand where to look for the right data and how to analyze it to get the expected results in the long haul.
Omnichannel vs. multichannel
While omni- and multichannel retail approaches are akin and both involve customer interaction across a variety of channels, they are not the same. The difference between them resides in the cohesiveness of customer experience throughout multiple touchpoints.
A great number of modern retailers already engage in multichannel activities; very few of them are truly omnichannel. Adopting the omnichannel approach is a complex and advanced process, and data tracking and exchange between various sales channels is one of its essential aspects.
How can retailers use big data?
Mordor Intelligence reports that by 2025, the big data analytics in retail is expected to reach $11 billion, growing at a 21% CAGR between 2019 and 2024.
Data analytics and marketing machine learning tools are bringing fundamental changes to retailers’ practices. Most applications of big data in retail are for business-wide cost reduction, the creation of a data-driven supply chain, and the integration and optimization of customer experience across all retail channels.
The following are the benefits that retailers can tap into when deploying big data solutions.
Targeted marketing
Big data, along with the adoption of machine learning tools in ecommerce, gives retailers insight into a 360-degree view of real-time customer behavior. Data points such as most profitable campaigns, conversions per promotion, and ROI per channel help retail brands tweak marketing strategies and optimize spend without dispersing effort.
Personalization of in-store experience
Retailers may leverage new sources of data, such as IoT sensors, sales log files, and social media sentiment, in order to improve how they interact with customers on the shop floor. By collecting customer intelligence through digital channels, retail brands can hyper-personalize in-store shopping for each buyer, thus fostering their loyalty.
A hallmark example of a brand that couples big data and predictive analytics in retail to transform its physical stores is Rebecca Minkoff. The company’s big data ecommerce strategy heavily relies on AI and social media to study customers’ preferences and expectations, while also leveraging data from in-store RFID tags to tweak products and enhance customer engagement.
Informed business development planning
Advanced algorithms can leverage big data, such as web browsing trends, to formulate a range of predictions that can support business decision-making in retail.
This is a strategy used by some of the most innovative brands, like Starbucks, that capitalizes on big data analytics to anticipate the growth potential of each new store, investigating metrics such as location, customer demographics, individual preferences, and historical market trends.
Stock optimization
Similarly, retail brands may leverage big data for stock optimization. Thanks to business intelligence, they can identify products that sell best and provide the highest return rate, as well as find items that underperform. This type of intelligence may also aid retailers in streamlining product replenishment to avoid overstocking and mitigate the risk of inventory shortages.
Combining market data with internal sales metrics also provides a way to set competitive prices to encourage both new and existing customers to purchase. In the case of existing customers, this can become a loyalty-generating technique: tailored discounts for each of the valuable customers are likely to entice their close affiliation with the brand. With big data ecommerce solutions, this process can be fully automated, so that it’s possible to serve “the market of one” just using a set of intelligent business rules.
What data should retailers collect?
First and foremost, big data can answer the burning questions about a customer’s behavior (products they liked, bought, added to wish lists; when they shopped; how they paid, etc.), personal details (location, gender, age, etc.), interests (what other brands they like, who their friends are, etc.), browsing patterns and activities (when they go online, what they like, what they review, etc.).
Analyzing this information gives retailers a chance to see what customers think of them, as well as if they are ready to buy or they need more incentives, such as special offers, discounts, and loyalty programs. This is where big data ecommerce intelligence comes into play.
These systems are designed specifically for making sense of data coming from dozens of touchpoints and identifying opportunities to optimize operational processes and customer relationships.
But enhanced customer intelligence is not the only benefit of big data for retailers. Big data also brings critical insights to retailers on the full spectrum of retail operation, from manufacturing to customer services and support.
Where to find this big data?
Retailers often hear that “data is everywhere.” But what does it mean exactly? It’s a platitude that doesn’t really solve anything for modern retail. In fact, research suggests that over half retailers claim they have no access to data, and over 42% don’t know how to use it effectively. This signifies that most retail brands are at a loss when it comes to identifying valuable data sources.
Retail is an environment that is particularly rich with data, and the number of data mining sources can be overwhelming. The higher the data quality, the more intelligence can be extracted from it, which makes the choice of the best sources critical for the success of any big data project.
Big data consulting can point retailers in the right direction and help them discover the optimal sources of meaningful data among the following:
- Point-of-sales systems
- Mobile devices
- IoT sensors
- Promotional calendars
- Open retail databases and reports
To be empowered with big data, retailers need support
Serving customers via multiple channels is only possible with the help of powerful big data ecommerce solutions. They are the tissue connecting diverse touchpoints and turning raw data into actual competitive advantages that stem from a better understanding of the customer.
However, realizing the potential of big data to build an integrated omnichannel customer experience can be challenging. One of the biggest barriers to big data implementation in retail is the complexity of the process that requires proven experience with similar projects.
Moreover, it’s expected that ecommerce predictive analytics, AI, and ecommerce SaaS will continue to revolutionize the retail industry further, which makes the selection of a robust, future-proof solution even harder. To add to this, while there are numerous big data solutions available on the market, which cover essential omnichannel retail, these solutions require careful tailoring to deliver their best.
To sum up, retail brands that wish to get up to speed with big data in order to get ahead of the competition, proactively meet customer needs, and enable retail automation may need assistance from experienced big data consulting advisors who have a track record of similar implementations and can take their business to the next level of greatness.
