All posts by aborek

Personalize Marketing with Digital Analytics

Once upon a time, everything was personal. When you went into your local bakery, you would hear something like “Nice to see you again, Mr. Richards. Since it is your son´s birthday in a few days time, would you like me to bake a birthday cake for him?” Today, every customer is one in a million of other customers. So, here is the trick question: how can you personalize marketing to thousands of your customers? The key is data. Digitize every touchpoint with your customer to collect data. Listen to the voice of your customer on social media. Give your customers rewards and incentives for sharing data with you. Look for data that explains the local context of your customer interactions, such as local news, events, economic situation, prices, culture, weather and political opinions. And integrate all that data to learn insights across all data silos.

Every single interaction with your customer matters. Use the data and insights to personalize every touch point that you share with your customer to improve the brand experience. Give your employees the power of analytics at their fingertips so they know what the customer has done online and on the mobile phone when the customer comes into the branch and telling the employee what the customer likes and what better to avoid. And use cognitive technologies that can fully personalize the automated interaction on mobile phones and online.

Then, you have to learn how to predict things that are relevant to personalize the customer experience. First of all, you have to creatively construct features as the raw data is not ready for analytics. For instance, calculate the age of your customer using the date of birth. Determine the product categories bought, the frequency of purchases and total revenue from transactional data. And turn the profession into an average salary for this type of profession, for example. Transactional data combined with customer and contextual data is extremely powerful. Predict the psychological profile of your customers. Predict where your customer are going and what they need at this very moment. And predict the next best marketing action for each individual customer.

Type of personalization Example
Personalize style for your communication “I am a fact driven person. So, please, just give me the facts.”
Personalize channel for your communication “We prefer personal interaction when it comes to our mortgage.”
Personalize content for your communication “You should know that I am a vegetarian.”
Personalize context for your communication (e.g. time, place) “I am on my way home and looking for a great place to eat.”
Personalize your service “I like action movies with Sylvester Stallone. No nonsense.”
Personalize your Product “My car can speak to me and knows my favorite restaurants.”


With the right digital tools, you can personalize nearly everything. The style of your marketing messages. For instance, your customer might be a fact driven person and expects you just to give him the facts. The preferred marketing channel. You might find out in your data that young couples that plan to buy a house might prefer personal interaction when it comes to choosing the right provider for their mortgage. The content of the marketing message. You better avoid selling meat to a vegetarian. And the context of the marketing message. An add for a restaurant works best, when I am hungry on my way home from work and looking for a great place to eat.

Besides your marketing, services and products can be personalized, too. In the old bakery times, a personalized service could be something like you coming into the bakery and the baker you´ve known for a long time says: “I noticed that every Wednesday you buy four pretzels. I have reserved some for you as we were nearly running out of stock.” Today, a personalized service is given to millions using customer data analytics, for example, by media streaming service provider Netflix that knows that you like action movies with Sylvester Stallone and maybe some movies with Bruce Willis, but certainly no romantic nonsense like Titanic or Shakespeare in Love. Personalizing your product in old time in the bakery could be your baker saying: “Last time you told me that you like bread with sesame sprinkles. I made some especially for you!” Today, it might be your BMW that calls you by name and knows your favorite restaurants


Big Data – Big Risk: Why Companies Need Total Information Risk Management

In our book “Total Information Risk Management: Maximizing the Value of Data and Information Assets”, we argue that data has become the major source of risk in most industries. Never before in human history, data could create so many opportunities and do so much harm to an organization`s success as today. Data has penetrated quickly into every corner of our society. We use data in higher volumes, of higher variety and velocity and from many new sources like social media and embedded sensors in real-time to drive a majority of our decisions. It has become the most important asset of the 21st Century, sometimes referred to as the “new oil”. Yet, the rising importance of data and information assets makes it also the major source of risk for most companies. Oil can catch fire. When companies finally start to understand the dangers sleeping in poor data and information assets often accumulated over decades and combined with new data from a variety of untrustful sources, it is often already too late: massive mis-investments, huge regulatory fines and permanent brand damages are only some of the consequences that cannot be easily undone once they happen.

Total Information Risk Management is a step by step guide for managers to identify and quantify the business impact of using poor data on business process performance and organizational success and how such risks can be mitigated. Solid measurement and quantification of data and information risk enables companies to generate real accountability and to treat data and information assets seriously and more responsibly. It also gives a great basis to build a convincing business case for data quality improvement.

A very typical situation is, for example, a manager who asks: “How many new sellers do I need to hire to meet my targets?” And the business analysts would come back after a while with the precise answer: “our analysis reveals that 3520 new sellers are needed”, which would lead the decision makers to reply: “Ok, this is interesting, well done, 3520 sounds very reasonable. But how reliable is the data?” The business analysts would assure that the analysis is rigorously conducted using data that comes from a system which is considered as a trusted source by most of the departments. The leadership team, being fully satisfied, would announce the new targets to the rest of the organization: “We need to recruit 3520 new sellers in the next quarter. This is grounded on a rigorous analysis of our business analysts!”

But, what if these numbers are wrong? Who would have time to verify if the methodology and the data that is used to calculate the results are indeed trustworthy and of high quality? And who would dare to question such “hard” facts indeed? And if something goes wrong, management can always refer to the business analysts. And business analysts can easily blame the data behind the analysis, the general complexity of the problem, and other external factors that influence the outcome of the decision.

Literally, millions of the most important decisions made by companies are executed exactly this way – every day. And an incredible amount of these decisions are mislead by poor data and sub-optimal analysis, leading to immense costs and risks in these organizations. There is a general lack of accountability and this is why huge risks are created in companies day by day – and why nobody addresses the true root causes of these problems. The formula is simple: Bad data leads to bad analysis, which leads to bad decisions, which leads to risks in operations and strategy.

So, how can risks from poor data be prevented? Companies can only protect themselves and make data and information reliable assets, if they start measuring the risks created by not having the right data and information of sufficiently high quality. Assessing risk caused through poor data and information assets will make the potential data and information risks tangible and visible to anyone – impossible to be ignored by the business part of the organization. Risk mitigation can then address the causes of the data and information risks with a targeted mix of technologies, transformation of the business environment and suitable information governance.

Leading companies are not the ones that simply use data to drive decision making, but those companies that assure that the risks hidden behind the data are clearly understood, measured and managed pro-actively.