Forecasting

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

Applied Insights ran a one day workshop in Predictive Analytics in association with the Emetrics Marketing Optimisation summit on 22^nd May at the Hotel Russell in London. A course outline is below.

Please contact us if you would be interested in joining one of our courses or developing a customised in-house training session on predictive analytics.

Predictive Analytics - course outline

An Introduction to Data Mining and Predictive Analytics is a one day workshop covering the foundations of this innovation marketing analytics discipline. During the course of the day you will gain a thorough familiarisation with some of the key principles and methodologies of data mining and predictive analytics and learn how to apply them to common marketing problems such as:

• How can I predict campaign response?
• How do I segment my website visitors or customers?
• How can I anticipate possible customer defections?

In this one day interactive course we will cover the following topics:

Introduction:

• What is data mining and how is that different to predictive analytics?
• How organisations are currently using data mining and predictive analytics across their businesses and to solve particular marketing problems

Processes and implementation

• How to go about a data mining/predictive analytics project
• An overview of a standard industry process (CRISP-DM)

Methods and applications

• An overview of the main types of data mining and predictive analytics applications:
  • Forecasting
  • Segmentation
  • Classification
• An introduction to main methodologies such as:
  • Time-series forecasting
  • Regression analysis
  • Decision trees (CHAID, CART and so on)
  • Cluster analysis
  • Neural networks
• Case studies and examples of how these techniques are used and deployed in both online and offline marketing is areas such as:
  • Retention modelling
  • Conversion propensity modelling
  • Visitor segmentation

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

At this year’s Emetrics Summit in San Fransisco, Neil will be presenting a session in the “Advanced Analytics Track” entitled ‘Cutting through the NOISE: Applications of data mining and predictive analytics’.

The presentation will be looking at the application of techniques such as segmentation and propensity modelling to better understand website visitor behaviour.

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

A return visit to Stockholm by Applied Insights this year. This time we’ll be giving a presentation at the Internet Marketing Conference on “Predictive Analytics - Why Bother?”. We’ve also been asked to be on the panel on the subject of Testing and Analysis and have been roped in to moderating a panel session on Web Analytics. Should be interesting…

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

At this year’s Emetrics Summit in Washington DC, Neil presented a paper entitled “Cutting through the NOISE: Applications of data mining and predictive analytics”.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In part one of this series, I examined visitor segmentation, a data-mining technique. Now, let’s look at how data mining can be used to understand important visitor behavior over time.

Quite often when we use Web analytics systems, we focus on what visitors do during a particular visit. The classic conversion funnel is a good example of this trendMost Web analytic systems look at the conversion funnel in the context of a single visit, that is, they report on how people got to page A, then B, then C, and so on within a single visit. This information is useful because it helps identify potential process areas that need improvement. But if we think about those times when a visitor might make multiple visits to a site before a conversion, the classic conversion funnel might not give you a true perspective on what’s happening.Take the example of buying car insurance online. In the U.K., it’s a very competitive business. Consumers typically shop around for quotes and go for the best value proposition. As a result, it’ s very unlikely people will arrive on a site and buy car insurance on their first visit. Maybe they’ll arrive from a search engine, check out the proposition, and bookmark the site for future reference. Maybe later they’ll come back, get a quote, and leave to compare it to other quotes. Hopefully they’ll return to complete the policy application process, and a sale is made.

A generic conversion funnel analysis will contain an amalgam of all three types of behavior: research, quote, purchase. As a result, you’re not seeing a true reflection of your ability to convert opportunity into value unless you analyze visitor behavior over sequences of visits, rather than just within the single visit.

If you work with Web analytics data, you know it’s hard enough to understand what’s going on when examining a person’s behavior in a single visit. Analyzing behavior over multiple visits adds complexity. Here, data mining and predictive analytical techniques come into play.

If we accept (as in the car insurance example) that conversion is often a multivisit process, we must understand the process’s key drivers over time if we are to influence that visitor’s behavior. We must find out what behaviors over multiple visits are most likely to lead to a successful outcome.

Using a decision-tree technique like CHAID can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.

) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.As with the segmentation approach described in part one, data must be in the right shape before an analysis is started. That requires extracting and summarizing data to key activities and events in each visit of the visitor lifecycle. I often think that data mining and predictive analytics are part art, part science. The art requires possessing the right data in the right format for algorithms to provide meaningful and useful results. In these days of automated analytics, anyone can produce a model. It’s a question of whether the model is good or not.

In working with these techniques, we commonly find there are a small number of highly influential conversion drivers over multiple visits. Naturally those drivers vary from site to site, but the importance of time is usually one thing they share in common. The time between the first and second visit, and the second and third visit, and so on, are quite often a good predictor of the subsequent outcome.

As the need to tune the online marketing processes continues, organizations must add capabilities to their analytics tool kit. Data-mining and predictive analytical techniques are firmly established within other marketing disciplines. Perhaps their time is now coming in the online world.

This post originally appeared on Applied Insights’ blog. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ blog into our own.

Over the past few years we’ve all been hearing more and more about ‘Predictive Analytics’ (PA for short). It is one of those terms - like Business Intelligence (BI), Customer Relationship Management (CRM), Enterprise Resource Planning, (ERP) etc. - that once coined, captures the essence of an area of business/research activity and gets a life of its own - particularly as software and services vendors use it as a means to more easily describe and, of course, sell their products and services.

A simple definition of predictive analytics

A simple definition of predictive analytics is that it’s an activity which allows us to quantify future events or actions. This quantification could be as straightforward as generating a list of customers who are likely, at a point in time, to behave in a certain way, e.g. to churn, to register, to buy or to respond to a particular mail, etc. Typically this list will be accompanied by a score which gives us the probability (or propensity) that an event will occur.
Alternatively the technique may give us a value, or set of values, e.g. a set of sales forecasts for a given product line in the coming week; furthermore the forecasts are often presented with confidence values, e.g. we might predict that we will sell 50 widgets on Monday but we can say, with 95% confidence, that we will sell between 45 and 55.

So what’s new?

One of the main characteristics of PA is that many of the tools, techniques and applications which it comprises are not actually particularly new. Credit scoring is one of the most well-known applications of PA. and credit scoring has been around for over 50 years.

Wikipedia details many of the other uses and techniques which can be described as ‘predictive’ though the current list there is by no means exhaustive. You will probably recognise a lot of them. In fact many of them come from the world of Data Mining and there is some significant overlap between Predictive Analytics and Data Mining, but there are also many differences; more on that later.
So is ‘predictive analytics’ just a new bottle for a lot of old wine? One of the new things is that there is an abundance of relatively new technology which can:

• Take PA to a wider audience by making the often complex algorithms more usable for less statistical/technical users.
• Provide access to a broader range of techniques through smarter user interfaces which map more closely to the analytical process in such a way that modellers (analysts, statisticians, data miners, demand forecasters, etc.) can more productively access data , test and develop models and ultimately deploy the best ones.
• Allow the results of PA (e.g. customer lists, scores, models, etc.) to be used more easily in decision making processes going forward.

For example, PA is being used to enable customer services representatives in a call centre to prevent customers churning with appropriate offers. It’s also be used in web site recommendation engines which can serve up relevant content based on what is understood of a visitor’s needs, preferences and previous browsing/buying behaviour.

This last point could mean generating models in a format (such as PMML) which can be more easily plugged-in to an operational process or integrating PA into other tools like CRM platforms.

There are also many new techniques and new applications for those techniques. The theories around Naïve Bayes and Robust Regression, for example, may have been around for a while but it is only recently that they have been available in an accessible commercial format. Techniques to automate the search for the best fitting time-series model are also quite contemporary. Applications such as SPAM detection and multivariate testing to optimise on-line marketing campaigns and page content are arguably the latest in a long line. Even if your potential application doesn’t seem to feature in any list of previous ones it could be worth exploring the potential that PA offers. There is a first time for everything!

So what can predictive analytics do for me?

PA is more commonly applied within organisations to address specific issues like preventing customer attrition or to identify segments of customers who will be more responsive to specific campaigns. One of its advantages is that it is usually possible to demonstrate potential ROI through the model, and if the model is a good one, the actual ROI when the model is deployed should not be too far from the prediction.

Increasingly though these techniques are being used more strategically to inform a range of organisational decisions and actions. Effective CRM programmes, for example, often use PA in a number of ways to anticipate customer needs and behaviours across channels and at different points in the customer lifetime.

So more sophisticated applications will not only identify good prospects for acquisition they will help define a series of interactions as the prospect develops into a loyal customer which enhance the customer experience and ultimately drive mutual benefit.

Getting more into predictive analytics

Despite the publicity I feel that applications of predictive analytics, particularly in business, are only scratching the surface today. There are a number of reasons for that; among these is the age old one that there is a gap between the available tools/techniques and the appropriate circumstances for business adoption. There is also a dearth of resource to help bridge that gap!

In future blogs I’ll explore predictive analytics in detail; looking more closely at the tools, techniques, applications and vendors. I’ll give a view on the process of applying PA - particularly aimed at those who haven’t started yet. I’ll also look beyond the hype into cases where it has been used to achieve significant results.

I’ll even discuss how PA relates to BI, CRM and ERP. Though we shall try and do that in a more understandable way than I did in that last sentence!

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In my last article I started to look at forecasting and investigated a set of methods called time-series techniques. Using these techniques you make your forecasts based purely on the patterns in the historical data of thing that you are trying to forecast. For example, you may looking to forecast the number of visits to the site next week based purely on the volume of traffic over the past few weeks. These techniques don’t take into account any external factors, they rely mainly on identifying underlying trends and seasonality to make the forecasts.

But what happens if you’re about to launch a major new TV advertising campaign next week? Won’t that possibly affect the amount of traffic to the site? You would certainly hope that it would but by how much?

This is where explanatory forecasting techniques come into play and have been used for many years in offline marketing analysis to understand the impact of marketing activity on important outcomes such as sales or brand awareness. These techniques build a model where the thing that you are interested in, such as visits, registrations, leads or sales, is explained quantitatively by external factors such as TV advertising, promotions, price and so on. This branch of techniques is often called Econometrics and one of the most popular methods is regression analysis.

A constant theme throughout these articles on more advanced analytical techniques is that the use of these methods is as much an art as it is a science. This is certainly true when it comes to techniques such as regression analysis. Regression analysis is widely accessible through programmes such as Excel and even in PowerPoint you can do some basic linear regression. An old colleague of mine used talk about these types of tools being like “putting guns in the hands of children”. Whilst I thought it was an arrogant way of making the point, the point he was making was quite valid. These algorithms can be quite dangerous if there isn’t the right kind of care and thought about how they are being used and what the results are saying.

The trouble with having these kinds of techniques available is that there is a tendency for them to be used in ways that are inappropriate or when the construction of the model or forecast hasn’t been fully thought through.

I got myself into trouble many years ago when I was cutting my teeth in the world of econometric modeling. We were doing some modeling work on price elasticity. We were trying to forecast the effect on sales of a proposed price increase on a client’s brand. The results of the model suggested a massive detrimental impact, bigger than anything that they had ever seen before. The client didn’t like the results (naturally) and said there must be something wrong with the model. I said that the model was technically correct according to all the diagnostic statistics but that I would go back and look at it again. At this point I noticed an event which I hadn’t picked up the first time round and which I hadn’t taken into account in the model. This event had made it look like that the brand was far more sensitive to price changes than it really was. When we factored this event into the model, the price sensitivity became something more appropriate and we were able to make a much more sensible forecast.

I took two key lessons of that experience:

1. If the model looks wrong, it probably is wrong
2. Modeling is like baking a cake

The first lesson was really the law of common sense. Whilst you are trying to look for insight through the use of these more advanced analytical techniques, the results should still make some sense at the end of the day.

The second lesson was that you need to make sure you have all the right ingredients in order to get the right result. If you are looking to forecast sales for example, you need to ensure that you are capturing as many of the likely impacts on sales as possible in your model. If you don’t then you will either inadvertently have the wrong effects coming through or there will be large errors associated with your forecasts.

Is there any use for these types of techniques in the world of online marketing? Well, much of online marketing analysis is based upon direct response or the tracking of individuals over time. This level of granularity is fantastic and allows us to get deep into the analysis of individual visitor behavior. I wonder though whether sometimes we can’t see the wood for all the trees. How do TV advertising and press ads influence online behavior. What can we infer about the synergistic effects of multi-channel marketing? These are all areas where I believe that modeling techniques will help us better understand the return on investment on marketing spend. As they in the offline world for a number of years.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

Over the past few weeks I have been taking a look at various analytical techniques that may be appropriate for understanding more about visitor behaviour than you may find in your average web analytics tool. Many of these techniques like classification and segmentation involve the use of statistical analysis tools. This week I’m going to continue in that vein by taking a look at the subject of forecasting and some of the techniques that be used to assess and understand future trends.

As businesses build up their data trends then the trends become more interesting and useful. One of the problems with a fast growing environment where all the charts are showing lines that go “up and to the right” is that is difficult to know what the underlying trends are and whether marketing activity is having any effect on this growth.

When it comes to forecasting there are two broad categories of forecasting techniques: Quantitative methods and Qualitative methods. The quantitative methods are based on algorithms of varying complexity and qualitative methods are based upon educated guessing.

Quantitative methods of forecasting come in two main types:

• Time-series methods
• Explanatory methods

Time-series methods make their forecasts based purely on the historical patterns in the data that have been observed in the past. Say for example you wanted to make a forecast of visitors to your site over the next few weeks; time-series methods will only use the historical data of visits to your site in the past to make that forecast.

Explanatory methods use other data as inputs into the forecasting data, so in my example above you might include marketing data as inputs into a model to understand how they impact on visit levels and use that data to also make forecasts for the future. These types of techniques have been used for ages in the offline world to evaluate the effect of marketing activity on brand awareness or sales.

Time-series methods are probably the simplest methods to deploy and can be quite accurate, particularly over the short term. Most quantitative forecasting methods look to explain patterns in historical data as a means of then using those patterns to make forecasts for the future.

Simple time-series methods include moving average models. In this case the forecast is the average of the last ‘x’ number of observations, where ‘x’ is some suitable number. For example if you were forecasting monthly sales data you might use a 12 month moving average where the forecast for the next month is the average over the past year.

The trouble is that simple averaging methods don’t tend to work well when there is either a trend in the data or there are seasonal effects. This tends to be the case in a lot of marketing data! In the case, other techniques such as exponential smoothing techniques may be more appropriate.

With moving averages every point in the data carries equal ‘weight’ in making the forecast. With smoothing methods more importance is placed on the most recent data than on the historical data for forecasting. So, if there is a trend in the data then it is going to use the recent observations to make up the bulk of the forecast and the forecast is more likely to reflect the trend.

Moving averages and simple exponential smoothing techniques are available in Excel and so are easy to execute. That’s one of the great advantages of time-series methods, they are generally simple, cheap to run and relatively easy to interpret.

There are more complex time-series techniques out there as well such as ARIMA or Box-Jenkins models. These are more heavy duty statistical routines that can cope with data that have trends and seasonality in them. You would probably need to invest in a statistical analysis package or a dedicated forecasting package to use these more powerful algorithms. Like any analytical technique though, you shouldn’t use them ‘blind’ and treat the results as gospel. In fact, all forecasts are invariably wrong. It’s just a question of how wrong they are!

So why would you use these types of forecasting techniques?

Forecasting techniques are often used as much for their explanatory power as for their predictive power. Understanding the trends and seasonal behaviour of your business gives you a better understanding of the underlying health of the business.

In consumer goods marketing for example, these types of forecasting techniques are often used to assess the “baseline” performance of the brand. A forecast is made of what the sales would have been in the absence of certain types of promotions or advertising, so that the underlying trends can be assessed.

Explanatory forecasting methods take the process a step further and allow you to relate changes in marketing activity to changes in outputs such as sales, brand awareness, registrations and so on. Here we are looking for “causality” and can use that to feed into forecasts but as a way of evaluating marketing response. We’ll take a look at this in more detail next time.

Till then…