PRINCIPLES OF MARKETING WITH BIG DATA

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1 PRINCIPLES OF MARKETING WITH BIG DATA BUD KEEGAN EXECUTIVE SUMMARY: Hurdles marketers must overcome to use the power of big data Steps taken to unleash the power of data and how they have been taken by different organizations BRIDGING THE COMPLEXITY GAP Catching up with data opportunities Based on the findings of an IBM survey of Chief Marketing Officers (CMOs): 67% focus on segmentations and targeting 56% focus on sparking awareness 71% of them are not well prepared for big data The complexity gap the distance between the progress and promise of big data and our current ability to take advantage of it. Businesses struggling with marketing strategies Individuals sorting through information to determine for example house purchases The kind of pressures businesses are under today to get their heads around both the promise and the challenge of big data are huge they are trying to outrun competiton in ways in which data can be used. DINING ON DATA: FOOD GENIUS Case study: Food Genius Food Genius took on the fast moving taste of consumers (diners). They track what motivates our interest and taste around the dishes that we eat. 1. Tracking customer dining choices at dining locations offering over menus 2. Reporting 16.3 million different individual menu items 3. Analysing what the new popular dishes will be in different food categories This has an impact on the entire food industry: Food processors create new products based on upcoming trends Restaurants can create new menus Retailers can plan how to stock and display new products they offer. Companies like FoodGenius help other companies close the complexity gap in various marketplaces.

2 DAILY DATA: THE MULTIPLIER EFFECT Kinds of data we generate without knowing it What kind of data are we generating when we go to the petrol station: Cost of petrol and the different types of it Amount of petrol going into our tank Amount of petrol remaining in the gas station tank Surveilance video from security cameras (insurance purposes, crime security) Credit card purchases: sale amount, purchase item, what other things we buy, personal data of the cardholder Loyalty card: relationship between the commercial establishment and client (age, background, frequency of shopping) Cash: particular transaction device, purchasing patterns and habits Receipt: time of purchase, location, total takings for the day Social media: ratings on social media, loyalty contests for fans All of this data gathered adds to the problem of analysis bulk and organisation. Companies have to pick through all of this data, analyze it, respond to it, use it (i.e. for tax purposes). CHANGING THE DATA FOCUS Shifting from brand to customer The primary focus for traditional marketers: data generated and focused around the brand How many units are being sold and how does it impact the budget What is the forecast for the future Nowadays, marketers are readjusting their aim from focusing on the brand to a customer centric model. Instead of focusing on the numbers themelves, they look at what the customer is interested in buying or considering Example: Coffee shop Customer centric: also sell bags of beans as an upsell opportunity (if a client enjoys the coffee, he or she will want to make it at home by themselves) Traditional will not pay that much attention to it at all Example: Walmart s Get on the shelf campaign Walmart allowed their customers to choose products that were to be added to the shelf space at the physical stores, on the online store, and to get marketing and production help. It got over 4 thousand entries. The public front of the campaign had suppliers getting on the shelves The backend data generated allowed Walmart to generate interest around products that they would stock in the near future creating over 1 million votes and producing a database that ranked

3 product categories they had never sold before. In the past, marketers had to use small data based on, for example, research groups, or potential shoppers who might indicate a potential preference for a product. Now, marketers have larger and larger batches of data on which to make decisions. More confident about planning their products and services Learning to better use big data to approach and address consumer needs DATA IN ORGANISATIONS Who s in control? What happens when brands acquire data from us and pull it into organisations when external data becomes internal? Example: Hertz Rent a car has done so. Traditionally, IT owns data in an organisation Privacy concerns Backup issues Disaster recovery issues Access and governance issues Nowadays, marketing department wants to use that data. When the two departments cooperate in utilising data, the company profits. The traditional paper based customer satisfaction survey people didn t have time to fill it out, or didn t really care about the information they added. Discovering a customer satisfaction issue was impossible the surveys would get stacked in a warehouse and not be analysed. Response: systematic, standardised extraction of this data in a process, reviewing mass amounts of data from customer surveys Philadelphia airport people were waiting too long for their cars * adequate staff put on duty to decrease wait times and reduce dissatisfaction to a minimum This required sharing of data between IT, marketing and customer service. The challenge isn t around having too little data, but about instituting the right big data approach, how we analyse, optimise and solve problems with it.

4 PRINCIPLES IN PRACTICE Important issues to take into account when thinking about how to use big data in an organisation: 1. Combine transactional and non-transactional data 2. Knocking down silos Real value from data can emerge 3. Transparency and real-time value from data Hertz Rent-a-car - using big data technology and methodology unlocks insights and solves problems in real time compared to the response time you have without using big data Walmart, for example, went so far to fund WalmartLabs - a subcompany dedicated to analysing big data for better performance of the business. EXAMPLE of their product: Shopycat Facebook-based application that reviews friends Facebook info to recommend gifts for family and friends, and allow sales (#1) - Walmart collects this data (social media data) and combines it with transactional data, resulting in real sales (#2) - Used and applied across silos - WalmartLabs shares the data across the organisation to better plan rollouts, introductions, price points and other activities (#3) - All data is real-time Using data, the app generates sales, and collects more data - therefore it gives back to both the company and the customer. DATA DRAMAS: FOUR COMMON PITFALLS Not all companies are quite as adept at handling the big data challenge. The most common mistakes are: 1) Taking action without any data - a lot of companies used to take this approach as no data was available, but now it should not happen as data is widely available. Hollywood movie flops 2) Having data, but not knowing what it means - data is not used well, it ends up stored away and not analysed. 3) Information without contexts - having answers, but not knowing what the questions are We know customers buy products at noon, but don t know where Computer mouse - invented at Xerox from the Palo Alto Research Centre, were not interested in commercialising it as they focused on enterprise problems instead of individual issues 4) Have data, but take no action - context without action

5 Companies that occupy top brands today and those that did 10 years ago - sometimes acceptance of actions data points to just does not match with social expectations I.e. 70 s American cars, produced bigger and more luxurious instead of the wanted smaller, more fuel-efficient models SOCIAL SENTIMENT: PICKING THE HITS No one s got big data right - but that would be a big ask at this stage of development. However, we can look at social sentiment, or how it is used to produce the entertainment we are seeing. Social sentiment is data found from Facebook or Twitter based on people s social media activities and is being used in many areas to allow companies to get a sense of how people are responding in social media to their products. Many brands use expensive or proprietary technology, but there are many free tools available to use. Social Mention - a search engine for social media content using text analysis to describe a brand/person/trend. Strength - the likelihood the topic or brand is being talked about Passion - how excited fans are (i.e. looks at words people use, terrific or horrible ) Reach - how widespread attention is on a particular subject In the links - other tools to look at social media. --> Jonathan Taplin - It is incredibly easy to predict how well a movie will do based on the buzz that surrounds something. Based on Twitter analytics, Meryl Streep got a lot of attention at the Oscars, but they were half and half sentiment-wise, whether Best Actor had a lesser buzz but more positive feedback.

Mobile Marketing. This means you need to change your strategy for marketing to those people, or risk losing them to your competition.

Mobile Marketing. This means you need to change your strategy for marketing to those people, or risk losing them to your competition. Mobile Marketing Introduction Mobile marketing is one of the fastest growing segments of online marketing. Over the last two to three years, the number of people who access the internet on mobile devices