Big Data: Foundation for Hyper-Personalisation
By Raj Dalal, Founder & CEO, BigInsights on Saturday, August 10th, 2013
Features in QESP NewsletterVolume 25 , Issue 1 - ISSN 1325-2070
Most of the business publications such as Forbes, Fortune, Australian Financial Review today carry articles on big data. Data generation and its analysis is a field that is coming to its own and has led to big data being discussed in boardrooms and the C-suite. However business and IT executives are still struggling to understand the complexities of implementing industry specific applications relevant to their organisations.
The hype around big data is comparable to the uproar made 15 years ago over the potential of Internet and e-Commerce in day-to-day life. Sceptics also had dismissed the implications in the online world, then. Yet, today, everyone has realised how the industries such as media and retail have been transformed by the Internet. Today, in the case of big data, sceptics are once again meting out the same treatment to big data. The manner in which Internet has transformed the world, big data retains the same ability to do so for enterprise, industries and individuals in the years to come.
No one actually wants big data but actually wants the “Big Insights” that could be culled from such data. These “Big Insights” need to be operationalized if they are to have any kind of impact on an organisation. While companies struggle to understand the benefits to the business, some retailers are investing heavily in the areas of big data to attract, retain and upsell to their customers using BigData technologies.
Hyper-Personalisation in Retail
Imagine if you were a boutique store owner servicing about 150-odd customers in a small town some 50 years ago. You would know a lot of about your customers’ personal tastes their likes/dislikes, who their friends were, in order to give them very personalised service.
Compare that to the mega retailers you see today. With 100s of stores, thousands of staff, millions of customers who have the choice of buying online and in store. So how do you achieve the same level of personalisation as seen 50 years ago?
Big data for predicting customer needs at Target
If you are a Lexus, Lululemon, Country Road, you may be able to build brand loyalty but if you are a retailer like Target in the US, it is hard for consumers not to think of you as a large convenience store.
The marketers at Target figured that if they knew when a particular customer was pregnant, they could “win” her over and perhaps earn her loyalty (read spend) for many years. They asked their analytics team if they it could “predict” when a customer was pregnant based on her spending at their store.
The “Data Scientists” at Target were able to identify about 25 products that, when analysed together (i.e. unscented lotions and soaps, supplements, soaps, cotton balls) allowed them to assign each shopper a “pregnancy prediction” score. More important, they could also estimate her due date down to within a small window (approx. two weeks). Now Target could send coupons timed to very specific stages of the customer’s pregnancy. Target marketers thought they had found the holy grail, right?
Their algorithms were so accurate that some fathers complained to Target store managers that they are “sending my teenage daughter coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” Much to their chagrin, many fathers later found that their daughters were truly pregnant.
Big data wars: Amazon versus Walmart
Amazon and Walmart are locked in a battle for the hearts, minds and wallets of US consumers. Amazon, a purely online business, had grown from a start-up to US$60 billion in sales in about 15 years. From a humble beginning as a seller of online books they now sell a wide variety of items in many categories themselves and on behalf of others. Amazon along the way has not only redefined the cloud computing model but has also been an innovator in the recommendation engines which account for over 30% of all sales attributed to Amazon. Walmart, on the other hand, is the world’s biggest retailer. With 4000 stores and $470 billion in sales (about $9 billion online) they have 100s of millions of customers visiting their store every month.
While competitor Amazon was first off the block in online commerce, Walmart was a slow to get into selling online. But in the last 3 years, it had spent over US $500 million establishing @WalmartLabs and buying tech companies that would give it the edge in e-Commerce, social and also the mobile sectors, using big data as the backbone.
If you think about it, in the last 20 years of retail, how people shop in a store has not changed. The question Walmart’s been asking is – how do you bring to a store the capabilities that have made e-Commerce so successful? With 200 million customers a week, if you can increase the average basket size by a dollar–that’s billions of dollars every year. In fact, it’s more than $10 billion–more than its projected annual e-commerce revenue this year.
Their latest acquisitions allows for active learning systems that combine real-time predictive intelligence, big data analytics and a customizable decision engine to inform and streamline business decisions. Their solutions include site personalisation, search, fraud prevention and marketing.
They want to make it easier for a customer to:
1) Plan before they come to the store
2) Make it easier to find goods when in the store
3) Make real time offers when they are in store for upsell/cross sell
Walmart mobile strategy is totally based on big data. It is not only simple but audacious. They want to make mobile tools that become indispensable for their customers while shopping in stores and online. One big-data feature Walmart is working on to include in its app is a improved shopping list function.
By leveraging big data, they are also developing predictive capabilities to automatically generate a shopping list for their customers based on what they and others purchase each week. It is like having a personal shopper with them throughout their shopping experience. Big data is the key to providing this sort of hyper-personalisation.
WalMart’s app already has a geo-fencing feature that senses when a user is in a WalMart store in the U.S. and prompts the user to switch to “Store Mode,” which is a setting that allows users to scan QE codes for prices and personalised discounts.
Walmart are working to take this even further and use big data to provide shoppers with useful information on demand. For example, when a Walmart app user is in the toy aisle searching for a toy under $30, the user could use a voice feature to tell the app its request, prompting the app to generate a list of the best-selling toys in that particular store that meet the requested budget requirements.
With more than 50% of its customer base equipped with smartphones, Walmart has already seen significant growth in the number of its customers using the Walmart app on their device while shopping in the store.
Using the Walmart app before and during their shopping experience brings Walmart closer to their customers.
Findings from the BigInsights Big Data survey
While big data has grabbed the attention of organisations and governments alike, business and IT leaders are struggling to understand the full benefits it has to offer.
The BigInsights “BigData Study 2013” revealed that 50% of organisations indicated “not understanding the benefits to the business” and “inadequate analytics skills in-house” as some of the top challenges their organisations faced when it came to big data. Deciding which data is relevant was also identified by over 40% of the respondents as a “pain point”
When asked about their understanding of the top benefits that big data could provide their organisation, over 60% respondents stated that improving insights about customers and decision making are big data’s key advantages.
Additional information about the Australia survey can be found at http://biginsights.co/big-data-study-2013/
Dealing with new big data technology platforms, integration with real-time data streams while ensuring privacy and security regulations are fully complied with are challenges given the limited in-house analytical and IT skills. BigInsights runs workshops for organisations to understand the opportunities, applications and challenges from big data technologies.
About the Author
Raj Dalal is the Founder and CEO of BigInsights. At BigInsights (www.biginsights.co) he leads a team of researchers and analysts to help enterprises and entrepreneurs capitalise on the big data opportunity. He has over 20 years of experience in IT across Australia & Asia Pacific working at companies such as HP, IDC and technology start-ups. He holds a Master of Commerce & Bachelor of Computer Science from University of NSW. He can be contacted at email@example.com or followed on Twitter @BigInsights.