THE INCREMENTAL APPROACH

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THE INCREMENTAL APPROACH

There are two main approaches to building a data warehouse. The first is to take the traditional waterfall-type route that was touched upon briefly in Chapter 1. The idea is that we gather the user requirements, using a standard methodology, then we go away and build the warehouse and, when it is complete, we deliver it to the users and move on to our next project.

The shortcomings of this technique are well-known, and there are special problems that occur in data warehouse projects where the waterfall approach is used. This is discussed in the next chapter.

The second approach is more akin to rapid application developments (RAD) and involves piecemeal development. In data warehousing, this approach is often referred to as incremental. Because data warehouses are different from operational systems, it is entirely feasible to adopt an incremental approach to development and implementation. If we were developing, say, an order processing system, breaking the system up into smaller pieces is quite problematic . Let's say our order processing system has the following major modules:

  1. Order capture

  2. Stock allocation

  3. Back-order generation

  4. Stock picking

  5. Packing and shipping

  6. Delivery note processing

  7. Invoicing

It is difficult to see how we might build, say, the order capture module and release it to the users, because there is nothing in place that will handle the captured orders. Similarly, there is little point in implementing back-order processing because it is dependent on the module that checks the stock figures. The problem is that all the modules of the system are so interdependent that it makes an incremental approach to implementation an impractical proposition.

We do not have this problem in data warehousing. The fact that we can build and release parts of the warehouse is another aspect that sets data warehousing apart from other systems.

We can use this special feature of data warehouses to powerful advantage in building the business case because it enables the following:

Pilot implementation.   This is a great way to start out in data warehousing. Developing a pilot enables businesses to dip a toe in the water in the sense that we can sample a data warehouse and start to get some value without making a commitment to spend vast sums of money. Once the organization can see some value coming out of it, it will be more sympathetic to the next increment.

Quick wins.   Quick wins are sometimes described as low hanging fruit. This is where we can identify something that is relatively easy to produce but that has a very high value to the business. In a CRMdata warehouse, it might be the ability to create market segments for customers and provide lists for targeted campaigns .

Prioritization.   An incremental development enables us to prioritize the deliverables based on a tradeoff between business value and ease of delivery. We'll be expanding on this later on in the chapter.

Defining the Increments

If we're agreed that an incremental approach is the best way to proceed, then we need to determine just what the increments ought to be. In principle, the answer to this is fairly obvious: the increments need to be whatever the business needs them to be. In other words, the definition of what we are to build is determined by the business.

If we followed an approach like the one described in Chapter 5 (Dot Modeling), then the way forward is not too hard. As a memory aid we developed a conceptual model based on the following three things:

  1. Business goals

  2. Business strategy

  3. Business information

We know that this approach provides us with a clear indication as to the business requirements and these were incorporated into the general conceptual model (GCM). We also know that, for the Wine Club at least, our EASI data architecture fully supports the GCM. The information that the business people will be drawing upon will ultimately be drawn from the data architecture, as this is what will ultimately be developed. So we are confident that what we propose to build will provide what the business needs, or believes that it needs. What we now have to do is get the business people to do their bit.

For each of the business goals, they have to be able to state the value to the business, in terms of dollars, of the increment should it be achieved. In other words: what is it worth? This is why we insist on the goals being measurable and time bounded. One of the goals that the Wine Club had was to reduce customer churn by 20 percent per year for the next three years . What the business people have to do now is figure out the value to the business of achieving that goal. Let us say that the churn rate is currently 15 percent of the customer base. That represents 15,000 of their 100,000 customers. So if they achieve their targets, then they will persuade some 3,000 customers not to defect within the next 12 months. For the purposes of simplicity let's assume that the customer churn occurs evenly throughout the year and so the Wine Club will lose, on average, about six months of sales for each churned customer. If we further assume that an average customer's annual revenue is about $500, then the revenue to be saved in one year is $750,000 (3,000 — 50 percent — 500). If the bottom-line percentage of sales is, say, 10 percent, then such a saving means $75,000 extra profit in year one. This becomes $225,000 in the second year ($150,000 from the customers we retained in year one and $75,000 again for those we retained in year two).

So that's what we get if we achieve the goal. However, we need to assess each of the business strategies that we were planning to adopt to see how the savings break down. Let's assume that the reduction of churn is to be achieved by three major new initiatives:

Loyalty bonuses.   For customers who have been active for more than one year, we want to be able to reward them, on their birthday and wedding anniversary, where appropriate, with a bottle or two of their favorite wine.

Personalized campaigns.   Once we have collected some information about customers' behavior, we want to be able to target them in campaigns with goods that we know will interest them.

Predictive modeling.   We want to be able to determine which of our customers are susceptible to churning so that we can take some proactive steps to try to ensure that it does not happen.

These three initiatives taken together should enable the Wine Club to achieve the 20 percent reduction in churn on a year-on-year basis.

The question that the business now has to address is:

For each of the three initiatives, what portion of the 20 percent target will it achieve?

Be warned that the business people will not always be comfortable with answering this question. But they must answer nonetheless. Really they should be doing this sort of thing anyway, and it's amazing to think that, generally , it doesn't happen. The amount of money that gets spent on little more than a leap of faith is pretty astonishing. One way to counter their reluctance is to ask:

If you don't quantify your expectations, then how will you know whether or not you've been successful?

The problem is of course that people often don't want to put their signatures to claims that, in the future, they might be called to account for. Their fear is that a day of reckoning will arrive and their boss will haul them into her office to administer some appropriate punishment for poor estimating. This is an understandable reaction, and we have to do our best to convince them that their job is not on the line if they get it wrong and that, if they give us the figures based on their best estimates, then we, the consultants, will take ownership of the justification from then on. So we are the ones who get it in the neck. Hey, that's what consultants are for!

Let us assume that we have overcome this seemingly insurmountable problem, and we can now say that the increased profit can be divided up as follows :

1. Loyalty bonuses 30 percent ($22,500 year 1, $45,000 year 2 onward)
2. Personalized campaigns 20 percent ($15,000 year 1, $30,000 year 2 onward)
3. Predictive modeling 50 percent ($37,500 year 1, $75,000 year 2 onward)

This can be generalized with the simple (and crude) formula as follows:

increased profit = additional annual income x proportion of goal%
x (number of years “0.5)

Using the formula, we can calculate the benefits for as many years as our customer wants us to. Some organizations will want to project forward just two years, while others might want 5 or even 10 years.

For each of the business strategies, in our dot modeling workshops we asked the business people to tell us what were the main types of information that they needed in order to implement the strategy successfully. This was where they were actually developing their own information model using the dot notation.

We can now see which of the information models the users have specified will bring about the biggest payback.

So far, we have looked only at one business goal, reducing customer churn. There are other goals, and we have to go through the same exercise with each of them. For instance, another goal of the Wine Club is to increase the customer base by 5 percent per annum (5,000 additional customers). The emphasis being on attracting the right type of customer.

The strategies for this are to be as follows:

  1. Customer profiling to try to ascertain the right types of customers to approach

  2. Campaign management so that we can contact the right people with the right offer

Using the same figures as before, we can say that the types of customer that we are trying to attract are those who should spend more than average. Let us assume that the average expenditure for these customers is to be $700 per annum. However, as has been said previously, it is much more expensive (maybe 10 times) to attract a new customer than it is to retain an existing customer, and so we must make some adjustment for this. In this case, we might expect to have to pay $20 for each customer.

Again, assuming that we attract our new customers evenly throughout the year, then the increased revenue will be $1.75 million (5,000 — .5 — 700). At 10 percent profit that will add $175,000 to our bottom line. From this, however, we have to subtract $100,000 for the cost of acquiring the customers, so the real net effect is $75,000 in the first year going up to $425,000 in the second year ($350,000 from the customers we recruited in year one and another $75,000 from the year two acquisitions).

Splitting the value of these two strategies should be quite straightforward. We can't do the campaigns effectively without the customer profiling, and customer profiling, on its own, is useful but won't help us achieve the goal. So we arbitrarily split them on a 50/50 basis. Table 8.1 shows how much each strategy will be worth over a two-year period.

One thing we're quite good at in IT is figuring out how much it will cost to build systems. Sometimes, of course, we get it spectacularly wrong. Those times when we do are well documented. Incidentally, we'll be taking a good look at estimating in the next chapter when we discuss project management.

Table  8.1. Grouping Strategies
Goal Strategy Additional Profit
  Year 1 Year 2 Total
Reduce churnLoyalty bonus $22,500 $45,000 $67,500
Personalized campaign 15,000 30,000 45,000
Predictive modeling 37,500 75,000 112,500
Recruit customersCustomer profiling 37,500 212,500 250,000
Campaign management 37,500 212,500 250,000

What we have done here, however, is to work out the value of the various components of the data warehouse, and this is something that, hitherto, we don't usually try to do. So instead of looking like some great black hole into which we will try to persuade our customer to throw money, we can now take a completely different tack and point to the potential lost opportunity if they don't invest.

Having said that, we still have to figure out the cost.

We have been building our incremental approach so far based on the concept of business goals and business strategies. In doing so we've kind of taken the view that there will be a one-to-one relationship between business strategies and deliverable increments. Well, that might sound OK in theory but life's not really like that. We usually find that, in systems integration and development terms, there are lots of crossing -over points that, if we ignore them, will end up causing us a headache with duplicated processes being built and lots of extra cost as a result.

As an example let us take another look at the five Wine Club strategies and also at the kind of data we need, and the source systems that will provide the data (see Table 8.2). Don't forget that if we've properly completed the dot model pages, then the information should already be to hand.

We have to take a pragmatic view of the data sources and the increments and try to figure out how to give the business what it wants, when it wants it, without wasting development time and money. There are three things to consider deciding in which order to develop the increments:

  1. The business strategy.   We have already discussed this. It's pretty clear that the business will tend to prioritize things based on the amount of profit they are expected to bring in.

  2. The degree of difficulty and, therefore, relative cost.   Sometimes, a high-priority business objective is very difficult to deliver quickly. In these cases it's a good idea to recommend that the first increment is something that is easier to deliver so that the business gets some real confidence about the value of information without investing too much of its cash.

Table  8.2. Correlating Strategies, Data, and Data Sources
Strategy Data Needed Source Systems
Loyalty bonus Customer circumstances Customer admin
Personalized campaign Customer circumstances Customer admin
Sales Order processing
Trips made Trip bookings
Predictive modeling Customer changing circumstances Customer admin
Customer profiling Customer circumstances and behavior Customer admin
Order processing
Trip bookings
Campaign management Bought in lists External
  1. The number of increments in which a data source is used.   Where a data source is used in more than one increment (this is very common), it is sensible to capture the data from the source that satisfies all the increments that use it. This may seem like common sense, and it is. However, it can help to influence the decision about what comes first.

The customer admin system is used in four out of the five proposed increments. In a customer-centric system such as CRM this should not be surprising; some might even say that it's blindingly obvious. Nevertheless, building the business case has to be done in a reasonably scientific fashion, as the data will, ultimately, have to be presented to financial analysts for scrutiny.

Let us assume that the total costs of the development and system integration have been calculated and are as shown in Table 8.3.

Table  8.3. Development Cost Summary
Source Systems Cost (in dollars)
Customer admin 280,000
Order processing 320,000
Trip bookings 100,000
External 80,000

The cost shown in Table 8.3 is for fully integrated systems, and initial, and fully automated incremental loading into the physical manifestation of the GCM.

The costs, and their split between increments, is shown in the table in Table 8.4.

Table  8.4. Breakdown of Development Cost
Strategy Source Systems Development Total Cost
Loyalty bonus Customer admin 70,000 70,000
Personalized campaign Customer admin 70,000  
Order processing 160,000  
Trip bookings 50,000 280,000
Predictive modeling Customer admin 70,000 70,000
Customer profiling Customer admin 70,000  
Order processing 160,000  
Trip bookings 50,000 280,000
Campaign management External 80,000 80,000

Now that we have the additional profit and the cost, we can easily calculate an ROI for each of the strategies. This is shown in Table 8.5.

Table  8.5. Return on Investment Calculated by Business Strategy
Strategy Profit After Two Years (in dollars) Total Cost (in dollars) ROI After Two Years (percentage)
Loyalty bonus 67,500 70,000 96.4
Personalized campaign 45,000 280,000 16.1
Predictive modeling 112,500 70,000 160.7
Customer profiling 250,000 280,000 89.2
Campaign management 250,000 80,000 312.5

Wow, most of our strategies look quite attractive, don't they? Look at campaign management. For every $1 we spend on an externally supplied list, we get over $3 in return after just two years.

You might ask whether or not we need to buy a campaign management system in order to take full advantage of the benefits of campaigns. The answer is that we can invest in a campaign management system if we want, but we absolutely do not need one in order to run campaigns. By far the most important aspect of running effective campaigns is selection of the right customers as targets for the campaign. Campaign management systems and other software are reviewed in Chapter 10. However, making sure that we target the right customers requires that we do our customer profiling properly. Both of our main business goals include strategies for the analysis of customers. In order to reduce churn we have to do some predictive modeling, and in order to recruit the best kind of new customer we have to do some customer profiling. One of the best ways to do both these things is with a data mining product. We might also want to do more straightforward analysis using a standard query product. If we decide to invest in such software products, then we need to factor the cost into our ROI calculations.

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Designing a Data Warehouse . Supporting Customer Relationship Management
Designing A Data Warehouse: Supporting Customer Relationship Management
ISBN: 0130897124
EAN: 2147483647
Year: 2000
Pages: 96
Authors: Chris Todman

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