Today, doing successful business is impossible without using multiple channels to reach and engage consumers. With almost equal numbers of Americans preferring either online or offline shopping (51% vs 49% respectively), the two channels need to be taken care of with no particular bias.
To successfully master this multidimensional approach, retailers have to adopt data-first ecommerce strategies. The question is where to look for the right data and how to make sure the discovered data would bring sufficient results in the long haul.
To answer these questions, this post will be looking at ecommerce from the big data consulting perspective to understand why big data analysis should become digital retailers’ top priority.
At an ecommerce enterprise, meaningful data resides everywhere. As illustrated by Oracle’s Sanjay Mehta, this is found throughout the supply chain and beyond, including merchandising analytics and social media sentiments.
The centerpiece of this information ecosystem is customer profiles, which are the key to financial sustainability: the more repetitive purchases customers make, the lower the cost per acquisition and the higher the profit. To make it happen, customer data needs to be put into action to create targeted and efficient messages propelling purchasing decisions and loyalty.
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 is retailers’ chance to see whether customers like their brands, as well as if they are ready to buy or they need more incentives: special offers, discounts, loyalty programs, and so on.
Also, knowing customers’ purchasing history and extrapolating the frequency and value of their orders to predict their future purchases is vital for understanding their lifetime value (LTV). This way, it’s possible to identify the top-spending and most loyal customer segment and tailor the brand’s outbound marketing accordingly to foster their retention.
This is where big data ecommerce platforms come into play. These systems are designed specifically for making sense of the data coming from dozens of touch points and showing the opportunities to optimize both operational processes and customer relationships. One imperative, though, is to make sure your business intelligence strategy around these solutions is omnichannel too.
Integration is the key when it comes to making big data for ecommerce work. At the data level, this means combining structured and unstructured data, including that from social media and IoT networks, into one master pool. At the software level, such integration is necessary to ensure there are no interruptions, duplications, and manual intervention needed to get clean and actionable data insights.
Another critical aspect is synchronization. Taking into account monstrous volumes of big data, it can easily go awry. For example, poorly synced data may result in erroneous inventory planning should the footfall figures lag behind and not correlate properly with the actual sales volumes.
This year’s McKinsey Analytics report highlights that big data is bringing fundamental changes to retailers’ practices in sales and marketing above all. The table below shows the degree of change to the retail industry's core practices brought about by data and analytics.
Sales & marketing
|Other corporate functions||Moderate change|
|Other operations||Moderate change|
|Capital-asset management||No/minimal change|
This goes in tune with ecommerce businesses’ heavy emphasis on mastering their customer-facing channels, where big data can help gain competitive advantages in the following ways:
Laser-sharp marketing—looking at such metrics as most profitable campaigns, conversions per promotion, and ROI per channel helps tweak marketing strategies and optimize spend without dispersing effort on ineffective action points.
Dynamic pricing—combining market data with internal sales metrics is a way to set competitive prices to encourage both new and existing customers to purchase. In case of existing customers, this can become a loyalty generating technique: coming up with tailored discounts for each of the valuable customers is 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.
Personalization—every bit of customer data should serve the purpose of ultimate personalization beyond pricing. This can span the choice of a preferred channel and store location, loyalty rewards, and product recommendations. Making sure your big data system is well-connected to the ecommerce website CMS and the enterprise CRM system is a crucial step if you want to create a truly consistent and relevant experience.
Customer service—big data analysis in this domain can benefit both customer experience and operational processes, as the details of customer inquiries reveal common weak spots and inefficiencies. In terms of the time-to-resolve ratio, big data ecommerce tools help cut it by bringing together all inquiry-related information from around the company in an instant.
Transparent delivery—by monitoring dispatch and delivery data in real time, it’s possible to create an added value for your customers through complete visibility into the state of their orders. This information can also be used to analyze delivery times and optimize relationships with contractors.
Stock optimization—as simple as it is big data analytics is a good way to look at the products that sell best either as standalone or in a bundle, to identify the ones that get highest return rate, or to predict the demand to replenish the stock accordingly. Again, when we talk about thousands of SKUs, business intelligence automation is the only method to fulfill such analysis in a reasonable timeframe.
Informed business development planning—holistic big data analysis, such as of demographics, historical market trends, and competitors can inform retailers’ decisions about openings or closures of physical stores, mergers, and acquisitions, or other strategic moves.
In most of the use cases described above, predictive analysis will be the method of choice for identifying the most efficient and reasonable actions directed at the future. This type of analytics only became possible with the advent of tools making use of both big data and machine learning. This is now a space to watch: with a few clear market leaders such as SAP, IBM, and Tableau, more tech vendors are polishing up predictive analytics to make the most of artificial intelligence.
Retailers have been experimenting with the mighty powers of big data for quite a long time now. The following case studies show the versatile implications of big data for omnichannel retailers, starting with the one from as far as 2013.
Big data can be successfully used to target customers on the emotional level by making them feel special and unique. This is what can cultivate loyalty and organic word of mouth, with customers turning into walking-talking ads for the brand.
Specialty retailer Free People recognized it back in 2013 when they introduced their multichannel app that helped the company grow their net sales by 38% through targeted customer engagement.
The app blended regular online shopping with social sharing. This social component leveraged big data, namely customers’ reviews and pictures, to build a bridge between Free People’s physical and digital locations.
With it, the brand catered to the audience that preferred showrooming at physical stores with a smartphone in hand. In-store, customers could use their mobile app to look up the reviews, other customers’ outfits and share theirs, which contributed to their sense of belonging to a community and fostered an emotional connection with the brand.
Warby Parker, a US brand of sunglasses and prescription glasses, found a way to use big data to overcome the wave of physical store closures and open their highly successful physical locations across Northern America.
In a time when thousands of stores are closing down (stats has it, about 7,000 stores were announced to shut down in 2017), this originally online retailer turned to customer data to find out where their audience was actually shopping, and that is how they launched their first perfectly located outlets.
For them, the relevant mix included the geo-specific data on population density and the number of those wearing eyeglasses, as well as those who previously shopped on the brand’s ecommerce site. Together, this helped identify business growth opportunities and turned out to be a safe bet with 63 locations in the US and Canada.
Rebecca Minkoff keeps earning media attention for being one of the most forward-looking and technologically minded fashion brands today.
The company is using big data and analytics to transform its physical stores and redefine customer experience for Millennials to make it more personal and relevant. As explained by co-founder and CEO Uri Minkoff at Bronto Summit, their big data ecommerce strategy heavily relies on social media to study their customers’ preferences and expectations, while also leveraging data from in-store RFID tags to tweak their products and personalize shopping for each buyer.
The brand’s long-term vision is to let their customers control the entire in-store shopping experience, from the moment they get greeted by a smart screen to the automated self-checkouts to be installed at select locations. And even after a customer leaves the store, the communication doesn’t stop—with its branded smart handbags, Rebecca Minkoff is set to deliver personalized content such as promotions and special event invitations to sustain the connection in a whole new way.
Serving customers via multiple channels today is only possible with the help of powerful big data ecommerce solutions. They are the tissue connecting diverse touch points and turning raw data into actual competitive advantages that stem from a better understanding of the customer.
Recognizing the potential of data-driven retail, brands have already started looking in the direction of business intelligence technologies, often reinforced with AI. To hear more about your opportunities here, feel free to drop us a line to get an expert guidance.