2006 customer analytics


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2006 customer analytics

  1. 1. Overview The Knowledge Economy Setting up for Analytics Customer Analytics Delivering Pizza Before the Phone Rings What is Customer Analytics? What Can Customer Analytics Do? Dr. Paul Bracewell Understanding the Customer 9th November '06 Summary 1 2 Perspective The Knowledge Economy Statistician By-product of the knowledge economy is lots Empirical Methodologies (data driven, simulation, and lots of data. approximation – close enough is good enough) Data is the currency of the knowledge economy Interpretation is key (business context) How to cash in? Comes down to how the data is processed. Focus is fully utilising the data (valuable business asset – numbers don’t have a political agenda) Just like any rush, there are plenty of people jumping on the band wagon (think of students at Pizza – fast food – not a lot of substance… Pizza-Hut, all-you-can-eat) 3 4 What is Analytics? Understanding Analytics Customer Analytics is not extracting customer numbers To understand customer analytics, requires a – that is simple data extraction and reporting (slicing & glimpse behind the scenes. dicing – how pizza is prepared) Processing It is understanding Customer Behaviour Purpose of Analytics Think of customer analytics as the interest to be gained from your currency Importantly – no interest is yielded by stuffing your investment under the mattress… Slicing and Dicing is more like counting your investment 5 6 1
  2. 2. Before Analysis Data Collection Purpose of Data Every phone call, Data Context Every purchase, Access to Data Every search, Cleaning/Manipulation (Known Data Problems?) Make sure the project isn’t set up to fail (it may generates data and is often sound as if customer analytics can deliver the world, but an expression of behaviour without preparation, it won’t come close) understanding that behaviour is your business advantage 7 8 Data Storage Data Manipulation Out of Sight, Out of Mind: Like that piece of pizza left under the couch Cheap storage means that lots and lots of data after a night with the lads, data manipulation can can be collected and stored without causing too often be forgotten. many problems. With vision, data manipulation can be Often this means data is stored in warehouses minimised for future projects (modelling mart) with no doors, no windows. Even in an air-tight container, pizza goes stale 9 10 Data Types What is Analytics? Long Thin (one row per event) Short Fat (one row per entity) More than simple querying and reporting, Customer Analytics is the use of statistical Most work in preparing data for analyses is techniques to identify pertinent information. converting long thin data to short fat data. Sponsors often neglect this important step Where are the results? Why are you taking so long? 11 12 2
  3. 3. What is Analytics (2) Effective Analytics Customer Analytics enables the following sorts of Customer Analytics requires five key elements: questions to be answered – Someone who knows the data (domain expert) Who will respond to a campaign? Someone who knows how to analyse the data (analyst) How many calls will a call-centre get? Someone who knows how to interpret the results in the What is an acceptable waiting time at a call-centre? context of the problem (end user) What flavour of pizza will customers want? What are the triggers of customer churn? Someone who can sell the results to the business (sponsor) NOTE: Future Tense – prediction (pro-activity) Tools – using rigorously tested statistical techniques (need to Find the useful information and get that out to the look beyond the cheese) people who need to use it. Two Broad Types of Modelling – Predictive and Descriptive The task is to turn databases into sentences 13 14 Using Analytics to Make Decisions Making Robust Decisions Statistical techniques are there to make Is there a real difference between working with numbers easier – show us $1677.58 and $2268.17? when to look and where. Consider the distribution of the data Humans are poor at detecting patterns in large data sets. Correct use of statistics avoids mistakes and bias. 15 16 Understanding Customer Behaviour Role of Slicing and Dicing Rugby, Pizza and Beer Fraud BEHAVIOUR Risk BEHAVIOUR 1. Customer Analytics Marketing BEHAVIOUR Churn BEHAVIOUR 2. Slicing and Dicing Bracewell, P.J. (2002). Implementing Statistics in a Diagnostic Coaching Structure. Research Letters in the Information and Mathematical Sciences, 3, pp. 79-84. 17 18 3
  4. 4. Fraud Fraudulent Behaviour New Zealand Insurance companies loose $150 With a credit card, you are responsible for repaying the debt. A fraudster is not responsible for the debt. Any million to vehicle fraud each year. They estimate funds lost to fraud are recouped in bank fees and interest. that only 10% is picked up… A nice example is a type of fraud called a BIN attack (BIN = Bank Identification Number) You pick up the rest in your premiums. Look at your credit card – 16 digits or if AMEX 15 (Visa For fraud to be profitable, it has to be abnormal. starts with 4999, Mastercard 5403) Software on Internet allows #’s to be generated. Fraudster Customer analytics can detect abnormal takes the generated #’s and tests them at various websites - typically for purchases less than $30 to find genuine cards. behaviour Then those genuine cards are hit – without the card holder knowing… Fortunately, with the use of customer analytics BIN attacks can be identified. 19 20 Minimising Workload Acquisition Risk Scorecard Potential Bottleneck if large number of cases Improved hit rates Increase value for Money of Investigation Fraud With Intent Accidental Compliant Individuals Involved Cost of Fraud 21 22 Marketing Interpreting Results Another similar field is marketing – all about understanding Intention of the data must not be neglected consumer behaviour. Selecting customers for campaigns. Recent study on the Results are only as good as the:- North Shore at reducing junk mail showed that only 2% of 1. quality of data people read junk mail. 2. understanding of data and process Better targeting, better response rates, better profits Listening to the customer – actions speak louder than 3. “translation” to audience words – this can be used to create campaigns. 4. audience uptake Work out which customers are likely to leave, but also establish why they are going to leave so measures/offers can be put in place to keep those customers. 23 24 4
  5. 5. Information Simplication Marketing Automation Intended audience Once these ideas/thinking have been embraced, Ease of communication the next logical step is further improvement of Pin-pointing areas of “real” interest process… Reduction of work-load …this improvement stems from ease of deployment (automation)… Solid platform for acting on insight …paving the way for solutions like Marketing Greater understanding Automation. 25 26 Summary (1) Summary (2) Those organisations which are open to finding Insight leads to innovation only if action is previously unknown but useful information are well placed to take advantage of the wealth of data in the taken. modern business environment Action can only occur if insights are robust and The power of customer analytics is only applicable practical. when an organisation is receptive to the underlying principles of this process Insights are only robust and practical if suitable tools are used, right personnel are involved and “It is hard to look at both sides of the story when there is frequent and open dialogue between all you are coming from one perspective” parties. 27 28 http://www.aclu.org/pizza/images/screen.swf 29 30 5