Using Big Data to Drive Customer Engagement. Andrew Claster

Transcription

1 Using Big Data to Drive Customer Engagement Andrew Claster

2 Process 1. What do we care about most? (e.g. reduce consumption overall or at peak times) 2. What tools are available to us to affect this? (e.g. price, communications, devices) 3. What data is available to us? 4. What hypotheses are we interested in testing? 5. Design analysis or experiment to test one or more hypotheses 2

3 Big Data What data do we have? - Usage monthly, daily, hourly, minute-by-minute - Payments offline and online - Personal information (PII) name, address What data is publicly or commercially available? - Voter file (name, address, date of birth, phone, etc.) - Census (avg income, demographics) - Real estate (address, owner name, purchase price, time in home) - Other commercial (presence and age of children, number of residents in home, modeled income) 3

4 What Can We Do With This Data? - Analyze consumption patterns - Compare to neighbors / similar users - Identify effective messages to reduce consumption at peak times - Marketing - Predictive modeling - Targeting - Segmentation 4

5 Big Data, Survey Data and Predictive Modeling Big Data Big Data Sales, marketing, advertising data CRM data Online data ( , online ads, Website) Social media data Consumer data (Acxiom, Experian) Small Data + Sampling Predictive Surveys + Modeling = Field experiments A/B testing Data and models can be updated continuously Individual-Level Targeting Sales, Revenue, Profit Corporate Reputation 5

6 Key Elements Use actual market outcomes (e.g. profit, revenue, unit sales) Build individual-level predictive models Target at the individual level Conduct randomized controlled experiments Validation process 6

7 Successful Analytics and Data Strategies Leadership and buy-in What does leadership care about most? What don t they know? Look for quick/easy win opportunities aligned to goals Recruit allies and champions Identify promising datasets Get to know the data Maintain focus on goals, results and deliverables 7

8 Analytics and Data Tools 1. Analytics Strategy Development: What are our goals? What can we affect to reach those goals? How can we use analytics and data to do this? 2. Internal Data Audit: What data do you have? Where does it live? What are you missing? What data is most valuable? What data hygiene and bias problems do you face? 3. External Data Testing: What s out there? What is it worth? (quantify return on investment) 4. Data Integration: Combining data across platforms online, offline, sales, marketing, advertising, CRM, etc. 5. Technology Tools: Facilitating, automating processes and reducing errors for better analysis and decision-making. 8

9 Analytics and Data Tools 6. Survey Research and Message Testing: What messages are most effective and should be tested in creative executions? What data can we collect on corporate reputation and other intangibles? 7. Ad Testing: In any format (online ads, social media, SMS, , direct mail, TV, telemarketing) what is our return on investment? How many dollars in revenue do we gain for each dollar of ad spending? 8. Experiments: What return are we getting on our current investments? How can we improve our messages, offers, creative execution or targeting? 9. Individual-level Predictive Modeling: For each individual, what communication (sales, marketing, advertising, etc.) is most effective what message/offer/product do we deliver, who do we deliver it to and how do we deliver it? (creative execution and medium) 9

10 Analytics and Data Tools 10.Online/Offline Integration: How do we use online data to drive offline activity and vice-versa? 11.Social Media and Other Online Data: What can we learn by mining Twitter? What can we learn about people who follow us, friend us, like us, link to us? 12.Simulators: What tools can we provide to decision-makers to help them identify the optimal price, offer, message and measure the effect on unit sales, revenue and profit? 13.Reporting, Visualization and Mapping: Do decision-makers have insight into what is going on? Do they know what products, offers or teams are over/ under-performing? What can we show them with quarterly, monthly, weekly or daily reports to help them make better decisions? 10