Scenario - bxp and best next action

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1 Overview

Next-best-action marketing (also known as best next action or next best activity), as a special case of next-best-action decision-making, is a customer-centric marketing paradigm that considers the different actions that can be taken for a specific customer and decides on the ‘best’ one. The Next Best Action (an offer, proposition, service, etc.) is determined by the customer’s interests and needs on the one hand, and the marketing organization’s business objectives, policies on the other. This is in sharp contrast to traditional marketing approaches that first create a proposition for a product or service and then attempt to find interested and eligible prospects for that proposition. This practice, direct marketing, typically automated in the form of a campaign management tool, is often product-centric, and usually always marketing-centric. https://en.wikipedia.org/wiki/Next-best-action_marketing


BNA (Best Next Action) is an area of marketing that is making the most of Big Data. Large volumes of information facilitate the creation of models for what should happen next. bxp is uniquely positioned to help in this area to address the obvious and not so obvious challenges of this area.


2 Background

There is mountains of material on this area.


So where to start?


Imagine a customer rings in to a sales person. What do they know about the customer? Nothing. A dialogue occurs and the sales person attempts to work out what is the best solution for them using a number of techniques. Good sales people usually have a patter which they can use for scenarios but these are built up over time.


Image a customer rings in to a customer care agent. What do they know about the customer? They should know an awful lot, but trying to collate all that information between the customer phoning in and the person answering the phone is where technology comes in.


Essentially BNA is using statistics / models to guess what is most appropriate for that customer to provide an upsell or cross sell opportunity. The more information you have the more tailored a response you can provide.


3 The challenges

The dark arts of marketing and predicting what should and should not be done is not easy. It is possible to make informed decisions with large data sets but the word to note is "informed". Every case is unique and all of this work should be considered "is more what you call guidelines, than actual rules !"



3.1 Challenge 1 - The Data

Access to data is the first challenge. If you want to build models you need good, up to date data. Garbage in, garbage out. Collating all of that data is going to be the first challenge.


bxp can massively help by having flexible and easily configured data structures for storing all manner of data.


Secondly, as there is usually a LOT of data to consider, speed is going to be important. bxp uses Oracle MySQL MyISAM as a data storage structure. This is a faster engine than most products on the market specifically for large read processes.


So at this point, we have a lot of data and we can access it quickly.


3.2 Challenge 2 - Creating models / segments

The next phase is to create models within the data. So what does this mean? Well, image a very simple scenario. We have a date of birth field, which allows us to work out an age. We have an address field, which allows us to group localities. We have a firstname, which (in general) allows us to work out gender. If they are an existing customer we know which products they have bought.

So

  • Age
  • Area
  • Gender
  • Product

From this simple set of data, we can use a number of different approaches.


3.2.1 The manual approach

This approach uses knowledge from sales people and experienced marketers who create "segments". Groupings of customers who are to be targets. There is a pro and a con to this approach

Pro

  • Quick and easy to generate
  • Specifically focused
  • Can be used to identify groups where we have no customer information yet
  • Can map to existing business processes far more easily

Con

  • No supporting evidence


Using this I could decide that I'm targeting teenagers (13 to 18). I will look in opulent areas, as they will have money to spend. Male only. We will sell them our "Hip and Trendy" package.

Using the same approach I could decide I'm targeting 30s (30 to 39). I will look in poorer areas, as they are hunting for bargains. Male and Female. We will sell them our "Super Saver" package.


3.2.2 The Data Mining approach

What is data mining?

  • “…the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data…” (Gartner Group)
  • “…the analysis of observational data sets to find unsuspected relationships and to summarize data in novel ways…” (Hand et al.)
  • “…is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization…” (Cabana et al.)


Data mining has been fuelled by several factors:

  • Explosive growth in data collection
  • The storage of enterprise-wide data in data warehouses
  • Increased availability of Web click-stream data
  • The tremendous growth in computing power and storage capacity
  • Development of off-the-shelf commercial data mining software products


It is possible to elicit Knowledge in Data.


“A particular customer transaction at the Ballymount branch on Friday the 14 August at 9:15am, listed a box of tea, a litre of milk with a total purchase price of €3.10 which was paid in cash”


  • Such a specific collection of facts is known as a case
  • Facts are essential raw material for the elicitation of knowledge from data
  • Knowledge can be identified as patterns or regularities in the data
  • Patterns (regularities) in data that can be expressed by a statement of the form (IF x THEN y) called a Rule e.g.
  • IF a customer purchases a box of tea THEN they will also purchase a litre of milk


This approach uses existing data and mathematical models to statistically generate models.


For each rule generated there is Support (how frequently these facts pops up in the data) and Confidence (for all the times they show up, how often are they seen together)


There are a number of data mining algorithms available, each with different focus for model generation.

  • Prediction
    • Classification
    • Regression
  • Description
    • Association rules discovery
    • Clustering


At the end of this process I will have rules. These rules are my segments


As with any method

Pro

  • System driven so doesn't require creativity
  • Based on facts
  • The larger the data, the more accurate the rules


Con

  • Can only base rules on facts in existence, so "creative" thinking not possible


3.2.3 The usual outcome

The usual outcome is a combination of both. Start with a creative / manual process. Collect data. Refine the model.


It is also possible to use surveys and market research to add more facts.


3.3 Challenge 3 - Measuring results

Ok, so we have some segments we're going to target. It always costs money to do marketing, so the most important thing to do is to create a Return on Investment model.

  • What is our base line sales figure? (i.e. what do we make without any extra effort)
  • What do we spend targeting this segment?
  • When do we put the plan into action?
  • What are the results after the plan was executed? (The difference between now and the previous base line, is the ROI)


However being able to accurately associate marketing spend with outcomes requires a lot of systems, steps, checks and integration. bxp can deliver start to finish results.


4 The Elements

4.1 Overview

Marketing drives communication into a contact point, say for example a customer care department.


Every piece of marketing has a unique code in it, to allow tracking. Some methods are more automated than others.


As soon as that contact arrives the BNA engine kicks in (described below)


The agent is presented with the information to enact the BNA

File:bxp datamining 001.png


4.2 BNA Engine

The engine has a number of choices for best form of operation, but essentially we have all the key elements. We just need to decide what is going to work best. Combinations of the following can also be used.


4.2.1 Scenario 1: Homework already done

Out of hours, a number of processes run. These processes are the application of the segmentation and decisions based on the classifications above. Essentially we predetermine based on the data we have what the best scenario to present it.


bxp implements these rules through the MetaData module which can be schedule to run out of hours.

Pros

  • Out of hours allows really complicated rules to be built.
  • Reduces operational overhead of real time calculation
  • Useful where systems do not have an API. Batch processes are used.


Cons

  • Not easily adapted with live information captured from the customer
  • Requires the data to be accurate or the wrong rules will be applied


4.2.2 Scenario 2: Doing your homework on the way to class

As the call arrives, you look up the key data and you run that data through a set of fast acting rules. As the record is presented to the agent, the BNA has been calculated in real time.


Pros

  • Rule sets are easily manipulated and changed in real time
  • Can have dynamically captured data added to the records
  • Can read from multiple sources in real time.


Cons

  • Not easily adapted with live information captured from the customer
  • Increases operational overhead of real time calculation
  • Requires the data to be accurate or the wrong rules will be applied
  • Lookups / copies of data must be available for real time interrogation


4.2.3 Scenario 3: Doing your homework in class

The most dynamic solution of all is that the BNA is calculated as the agent is able to ask the customer questions to compensate for lack of data. The data added is in real time run through the rule sets and a NBA calculated live.


Pros

  • Rule sets are easily manipulated and changed in real time
  • Dynamically captured data added to the records
  • Can read from multiple sources in real time.
  • Live data can make sure BNA is best available with live information
  • Adapts for bad / out of date data sets.


Cons

  • Not many!
  • Increases operational overhead of real time calculation
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