Optimization step 4: setting the right boundaries

Over this blog series: “The Journey toward direct marketing optimization” I have covered all the topics that are part of the optimization process. As said before, however, the most impactful part in an optimization problem is to set the constraints.  If a company does not have any type of boundaries when selecting and contacting customers, then there is no optimization problem at all.

As a customer intelligence advisor, I have never worked with a company that does not have some contact policy restrictions. Most marketing teams do manage customer saturation and choose not to contact their customers more than certain number of times during a period. Another frequent constraint is channel capacity, particularly call center capacity. Most companies choose this channel as it is usually more effective, but it is also the more restrictive.

In the example used in the first article “The Journey toward direct marketing optimization” I already introduced two different type of constraints: contact policy and amount of targets per offer. In this last stop before we reach the destination, I will go deeper into restrictions and how they influence the optimized results.  As in SAS Marketing Optimization restrictions are classified as business constraints and contact policies constraints, I will use the same categories.MO_Journey_step4


Observing Contact Policies

In order to cover this type of restrictions, I will use the same simple example used at the beginning of these series of articles.

Two customers and two offers - not as easy to optimize as you'd expect.

Remember that if there were no constraints, we could have an expected profit of 265, the sum of all the possible offers for all the customers. However, the company in the example indeed has a contact policy in place. The company cannot contact the customer more than once, so we cannot select the same customer for the two available offers. In this simple example, having this restriction made us lose 105 of profit as we selected the most valuable offer for each customer (offer 1 in both cases).

It is important to differentiate between two different types of contact policy restrictions. The first one makes a customer eligible for optimization. This is relative to the target selection process and not optimization, as we discussed in the second stop of our journey, “Designing Campaigns for Optimization”.

When setting the contact policy in the optimization scenario, we are considering the contact for the period we are optimizing, not the previous ones. This is necessary because a client might be eligible for more than one campaign, so we have to decide how many times we are going to contact him/her during the period under analysis.

Contact policies can also be different by channel. Many companies we work with don't count email campaigns as a contact, so if they have different campaigns that are communicated through different channels, they can give their client more than one offer in the period optimized. The most restrictive channel is usually the call center. Most of the companies set that a customer can only be contacted once by an agent in the period.

Setting the contact policy in the scenario is very important in the optimization process. Knowing the company’s boundaries is essential as it affects the optimization results. The contact policy can be as complicated as necessary. SAS Marketing Optimization will take all the information in order to calculate the best results, with no need of complicated processes.

Managing Business Constraints

Contact Policies are not the only restrictions companies have. Many other business constraints have to be considered in an optimization problem. Continuing with our example, the next restriction we had was the minimum amount of customers per offer. Even for our very simple example, setting this restriction makes the problem more difficult to solve. By defining that each offer must have at least one customer, we have to change the solution and we are now able to get 150 as a result (offer 2 for client 1 and offer 1 for client 2), 10 less than the previous scenario.

Constraints can include, among others, budget limits, channel capacity, risk limitations, amount of offers, etc. These constraints can apply to all the customers or we could set constraints by segments, cities, or any other characteristic of the customer. For example a bank might need to limit the amount of loans offered to customers with a higher risk profile, even if they might probably the most likely to accept them.

Constraints can be “at least” or “at most”, giving the users the possibility to analyze different combinations and possibilities. For example, a Product Manager may want a minimum of customers for his offer, but also a maximum in order to control expenses. SAS Marketing Optimization will find the best number of offers between these boundaries.

In order to set certain constraints we will need particular data to input to the tool. For example if we have a budget limitation, we need to know the cost of each offer. This information can also be used when setting the optimization goal. If we need to set restrictions on channels, we need the capacity of each of them. The more information we have, the more complex we can make the scenario. However, be careful, complexity doesn't always mean better results.

All these kinds of factors are usually not considered in prioritization strategies as they are very difficult to implement. Implementing SAS Marketing Optimization can help marketers to target the best customers, with the best offer for each, observing all the business restrictions, and getting the best results overall.

We are coming to the end of this exciting journey. In the next and final stop I will explain how to put all these pieces together. However, as you might have noticed, Marketing Optimization is not only about having the best solution in place, but also about rethinking the campaign process. I will then cover this topic as well in my next article.  Join me!

Editor’s note:

If you did not read the previous posts in this series, I encourage you to do so since Luciana planned them as a step-by-step journey. Marketing optimization is a very effective way to tie overall business objectives (often profitability) to marketing campaign activity because it mathematically calculates the best aggregate outcomes based on how you define them. If you'd like to dig a little deeper into how marketing optimization could work for you, I suggest you download this whitepaper, Improving Multichannel Marketing with Optimization. Among other useful content, it includes a practical checklist of seven steps to optimize your marketing.

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Optimization step 3: setting the goal

We are now in one of the most interesting stops in our journey towards marketing optimization. We have been talking about optimization on the assumption that we have an optimization goal, however, as strange as it might sound, companies do not always know the overall goal of their direct marketing activities, much less how to measure the results. Although the ultimate objective is usually to sell more, doing that does not always deliver the highest profit for the organization. In fact, each campaign usually has its own objective and when combined together they might even have contrasting objectives.

In the first post, “The Journey Toward Direct Marketing Optimization” I explained, through a very simple case, how to maximize the expected profit, where:

Profit = probability of response * offer value

Then I covered how to measure the probability of response in my second post, “First Stop: Identifying the best customers through analytics.” Now we'll address what we can optimize and what information we need in order to do so for the second part of the equation.




Almost every company does direct marketing campaigns in order to increase sales, profit or revenue. The optimization goal in these cases is to maximize one of these measures. However, most of these companies don't actually know the income that they will get from a customer if he/she accepts the offer.

The optimization process will make that determination better if we can measure each customer/prospect we are targeting. Exactly what that means depends on the industry we work in and the type of products/services we are offering:

  • In telecommunications, incremental ARPU (average revenue per user) is a common measure that shows the extra value a current customer will bring if the offer is accepted. Incremental ARPU is relatively easy to calculate for plan upgrade campaigns, but more difficult for other type of offers.
  • At some banks we have worked with, we have used the Net Operating Income (NOI) of a customer as a measure to maximize. However, measuring the incremental NOI as a result of a certain product is a very difficult task, so we use the current NOI.
  • In the Insurance sector, one of the typical ways of valuing a customer is by doing a Customer Lifetime Value (CLTV) model. Then we can use this number in order to set a value for the campaign we are doing.
  • In Retail optimization, it is easier to calculate the value of a campaign if the offer is a specific product, because in that case it's the product's price x units sold to calculate the impact. In other industries or other scenarios, offer values are a bit more complicated to calculate.


Although is more common to set maximization goals, it's also possible to have minimization as a goal. One of the most frequent measures to minimize is the cost of the campaigns. If we have several campaigns and different channels to use, we could choose to minimize the overall cost of all campaigns. We all know that an email campaign is much cheaper than a call center campaign, but probably contacting the customers by emails is less effective. So we may want to have a balance between cost and expected response, which is possible by setting constraints. We will come back to this topic at the next stop in our journey.

Like for the maximization goals, the type of measure we need to minimize varies by industry. For instance, it is very frequent in the Banking industry to try to minimize the overall risk. If we are offering risk products, like loans or credit cards, we can measure the risk of the customer and try to minimize the overall risk of these campaigns as a goal. In the Retail industry, many campaigns can be discount offers, so we could need to minimize the overall discount we are giving to our customers.

Combined Goals

Sometimes one single optimization goal is not enough and we need to set secondary objectives. Going back to the risk example in banking, our main objective could be to maximize profit, but to minimize the risk of the offers. In most industries, a very common goal is to maximize revenue and minimize cost, and it's important to note that by maximizing profit instead of revenue, the outcome minimizes the applicable costs.

Controlling this in a prioritization process is certainly very complicated. SAS Marketing Optimization can work with two optimization goals in order to calculate the best result for both. This is of course more time consuming as a simple optimization, but very easy to perform through our software.

Whichever the goal is, we need information to use as an input in the tool. Yet, as said before, it is not imperative to have perfect information in order to optimize campaigns. Like with probability scores, we have alternative ways to measure the results. With the customers we are working with, generally the value is set by offer and not by customer. This is already a step forward as most prioritization processes do not consider this when selecting customers.

If choosing any customer as a target will produce the same results (they all have the same value), then the probability of response becomes more important to differentiate among the customers. If the probability of response is not a predictive model but an average response rate for all the customers, then constraints and contact policies will be more relevant to define the optimized solution.

Constraints and Contact Policies are in fact our next stop in this journey.  If we could do whatever we wanted, without restrictions, then this journey towards marketing optimization would not be necessary. Therefore, our next stop is the key to understanding optimization. I invite you to keep on travelling together. We are very close to our final destination: Optimized Campaigns!

Editor’s note:

If you did not read the previous posts in this series, I encourage you to do so since Luciana planned them as a step-by-step journey. Marketing optimization is a very effective way to tie overall business objectives (often profitability) to marketing campaign activity because it mathematically calculates the best aggregate outcomes based on how you define them. If you'd like to dig a little deeper into how marketing optimization could work for you, I suggest you download this whitepaper, Improving Multichannel Marketing with Optimization. Among other useful content, it includes a practical checklist of seven steps to optimize your marketing.

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Gartner names SAS a Leader in digital marketing analytics

SAS shown in Gartner's leaders quadrant.

SAS is a leader in Digital Marketing Analytics.

Digital marketing analytics has evolved over nearly two decades, per Gartner, and has beginnings with Web analytics developed to analyze server logs. Much has changed and a broader digital marketing analytics ecosystem has evolved into its own right, so Gartner saw the need to evaluate providers of digital marketing analytics solutions.

In their inaugural Magic Quadrant for Digital Marketing Analytics, Gartner specifically cited four areas of development to take note of: channel convergence, personalization & other advanced requirements, attribution & ROI, and the need for improved usability.

SAS was named a Leader in Gartner Inc.'s inaugural Magic Quadrant for Digital Marketing Analytics (DMA) - one of only three vendors achieving this distinction. According to Gartner, SAS offers "...a suite of solutions that addresses the full digital marketing analytics life cycle, including data collection, ingestion, exploratory analysis and modeling, simulation testing and optimization, decisioning, and measurement".

What is Digital Marketing Analytics?

Digital marketing analytics platforms are specialized analytic applications used to understand and improve digital channel user experience, prospect and customer acquisition and behavior, and to optimize marketing and advertising campaigns, with an emphasis on digital channels and techniques. They are stand-alone, end-to-end platforms, performing functions from data collection through analysis and visualization. They have demonstrated relevance to marketing through their ability to collect and ingest data from common marketing sources, provide tools for standard marketing analytics use cases, and have significant adoption by marketing practitioners.

Five Areas of Digital Marketing Analytics

As Gartner defines it, digital marketing analytics encompasses five key areas. Read More »

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Optimization step 2: designing campaigns

In this journey to direct marketing optimization, we have already gone through two important concepts: understanding what optimization means and differentiating customers through analytics for optimization.

It is now time to make a stop to think about the campaign design process. We will describe how we need to plan our marketing activities in order to make the most out of the optimization process.Image depicting a step 2 on a 5-step journey.

Typical Target Selection Process

In order to select the targets for campaigns, most companies use campaign management solutions. In this kind of solutions, users can build campaign decision flows to select those customers that are eligible for a certain offer. The decision flows will typically have several selection rules to check basic descriptive variables like gender, product holding, city, segment, etc. More analytical driven companies will also use predictive scores in order to select the best customers for their campaigns.

Another important selection filter is to check contact policy rules. Customers that have already been contacted last month with a similar offer might not want to be bothered with the same offer, so they are not included in the target selection.

During any given month, companies can run dozens or even hundreds of campaigns. Read More »

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Supermarket wars: now data-driven, loyalty-focused

In the past, the supermarket wars were fought on location and floor space. The biggest supermarkets had the most space in the best locations. Today it’s very different as battles are won and lost on price, loyalty and maintaining profit margins.

The price of milk rose to the top of the headlines recently, with some supermarkets reportedly lowering their price to the detriment of their suppliers. Politics aside, the reason supermarkets are tempted to lower prices is to attract and keep customers. If the milk is a good price, what else might a customer buy during the same shopping trip?

Today's supermarket wars are accelerating and increasingly data-driven.The rise of discount stores is seemingly a sign that customers prioritise price over range. Aldi, for example, has roughly 3,000 different products in its stores, significantly less than its UK competitors that can have around seven to 10 times more. But the milk debate and subsequent consumer commentary revealed that though customers have been voting for discounts with their pennies, their heads tell them that they wouldn’t really mind paying more. It’s about perceived price and value, rather than actual price.

This is no small issue. Earlier this year it was reported that the ‘entire structure’ of the supermarket landscape has changed because of the price war. Discount supermarkets are picking up more and more market share - Aldi now has 5.6 per cent, Lidl 4.1 per cent (a combined 9.6 per cent), pushing Waitrose down to seventh place with 5.1 per cent. This leaves a conundrum for non-discount supermarkets in which price must remain a priority, but where loyalty and range are also factors.

New games, new rules

Today’s battlegrounds are knowledge of customer preferences and behaviours and creating and keeping loyalty. The supermarket that knows its customers best can create a price point and a range that best suits them. That could even be at the hyper-local level too as high street space is at a premium and therefore must be used wisely. Read More »

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Optimization step 1: identify the best customers

Welcome back to our journey toward marketing optimization! If you missed the introduction, I previously explained the concept of direct marketing optimization in my last post, "The journey toward direct marketing optimization.” From that departure point, the topics in this post will take us to the first stop in the journey where we'll explore how to optimize the target selection.


I'll start by explaining how to understand the value of each customer for each offer using basic and advanced analytics. Even if you are a campaign manager and already familiar with this approach, let's quickly review it because it's vital in order to feed the optimization algorithm with appropriate data.

Differentiating customers through analytics

When selecting targets for campaigns, we need to know which customers are more suitable for each of the campaigns. Think of "suitability" as the probability of the customer to accept the offer. This probability will be more or less accurate, depending on the analytical methods we use.

In order to explain this, I will use the same example I used before. We have a scenario with two offers and two clients where:

Profit = probability of response * offer value

Two customers and two offers - not as easy to optimize as you'd expect.

A customer can be more suitable for a campaign because he/she has a higher propensity to accept, or because the value it can generate is greater. The value we need to assign to a customer depends on the optimization objective we set. We'll will cover that idea in a future post, and for now let's focus on the probability of response. Read More »

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Marketers ask: What are some of the pros and cons of a data lake?

If a data lake isn’t a data warehouse, as I proposed in my last post, then it behooves us to better understand more about this “new” data lake structure. In the fifth and final post in this series titled, Big Data Cheat Sheet on Hadoop, we’ll highlight some of the pros and cons of a data lake using a SWOT diagram.

Question 5: What are some of the pros and cons of a data lake?

This discussion comes from an online debate I had earlier this year with my colleague, Anne Buff, where we discussed the pros and cons of a data lake in context of this resolution: The data lake is essential for any organization that wants to take full advantage of its data. I took the Pro stance, while Anne took the Con stance.

Even though our online debate was focused on the data lake, it forced us to address the larger discussion of managing growing volumes of data in a big data world. With the onslaught of big data technologies in recent years—the most popular being the open source project, Apache Hadoop—organizations are having to look once again at the underlying technologies supporting their data collection, processing, storage, and analysis activities.

The Hadoop-based data lake happens to be a popular option right now. The SWOT diagram below identifies some of the key factors when considering a data lake. Keep in mind that this is just a quick snapshot (with brief explanations following), and not a comprehensive list:


Read More »

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Six ways to have authentic content

Starbucks is a company that has amply demonstrated that it’s about far more than pouring a cup of coffee. Their famous CEO Howard Schultz has boldly led the company to take on social impact initiatives that give their customers the opportunity to participate in those initiatives. Not all of the initiatives have been universally embraced, and others have had big positive impacts.

Rajiv Chandrasekaran interviewed at Content Marketing World.

Rajiv Chandrasekaran interviewed at Content Marketing World.

One initiative that has rippled far and wide was one driven by content, and it’s the collaboration between Mr. Schultz and an accomplished journalist, Rajiv Chandrasekaran that began with the book, For Love of Country: What Our Veterans Can Teach Us About Citizenship, Heroism, and Sacrifice

The book tells stories of the courage, dedication, and sacrifice of American veterans on the battlefield and their equally valuable contributions on the home front. The project resulted in the story retold in videos and other media and was promoted through the Starbucks brand and its vast network of over 7,000 locations.

Good lessons about how to create content emerge from the story of For Love of Country - and not necessarily content related just to social impact. Rajiv shared these insights with the sensibilities of a journalist and the story highlights how important authenticity is to any content, along with context. This is important because the media landscape today is changing, and now almost anyone can be a content creator. And forward-leaning brands are becoming both content creators and curators.

Authenticity is also important because our society is changing, particularly millennials – social causes and finding meaningful engagement beyond the transaction is increasingly important. And talent is not always driven by money, often it’s about shared values and the opportunity to do something creative and/or meaningful. So what are some ways you can have authentic content? Rajiv offered six: Read More »

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3 easy steps to great content marketing

I learned the hard way years ago that writing succinctly is hard work – and worth all the effort. Thanks to a stringent marketing professor who insisted all case write-ups be submitted on a single sheet of paper, I developed a genuine appreciation for good, clean writing and a similar affinity for someone who can speak that way as well.

Ann Handley, MarketingProfs

One such person is the marketing thought leader Ann Handley, Chief Content Strategist for MarketingProfs. Ann has written and spoken much about many aspects of marketing, and has a particularly well-honed expertise in content marketing. I am very pleased to share some of her pearls of wisdom on content marketing from a recent conference session.

For content marketing, Ann believes that quality and quantity are not mutually exclusive, and great content can scale if you get the basics right with these 3 easy steps:

  1. Bigger stories,
  2. Braver marketing, and
  3. Bolder writing.

Recent research by MarketingProfs and Content Marketing Institute shows that 30% of B2B organizations know that their content is effective, and that creating engaging content continues to be the top challenge that marketers face. The most acute challenge is creating content for new platforms and media that don’t feel like advertising and are actually the kinds of content our customers want. Read More »

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The journey toward direct marketing optimization

Today most companies engage in direct marketing to communicate with their customers. If you work on a campaign management team, you are probably sending emails, SMS, letters (you can admit it -  you are still doing it!) and also using call centers in order to extend offers to your customers.

Some companies use analytics to choose their targets in a data driven way, while others rely their business knowledge and mostly use rules to do so. Furthermore, in the same company the strategy can vary among Product Lines or Departments. Whichever your case is, all companies face the same problem: too many offers for too few customers and limited resources.

The need for prioritization

When selecting targets for the different offers, it can happen than a client can be eligible for more than one campaign. In a "perfect" world we'd have unlimited call center agents, an unlimited marketing budget and customers would not mind being contacted many times with multiple offers or even the same offer over and over. As we all know, that kind of "perfect" world doesn't exist.

Most of the companies we work with develop prioritization strategies in order to decide which offer the customer receives. Prioritization is a hierarchical approach that usually involves creating rules to pick customers for each campaign.   Although prioritization mostly handles contact policy restrictions, it usually does not take into account overall marketing results. We can illustrate this situation with a very simple example.

Consider the scenario where there are two offers that can be extended to two customers. We know the value we could get from each offer if the customer accepts the offer and we also know the probability or response rate for this offer.Two customers and two offers - not as easy to optimize as you'd expect. Read More »

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