What do Businesses Do with Their Data?

Collecting Data is easy…but applying analytics to better understand customer needs and wants is not so easy! It all boils down to what valuable information you need to better understand your customer. There is so much data that you earn, accompanied by data available from the outside, so a right balance needs to be struck! You need to find what really works for you, if not, you will possibly be caught in information overloading with Big Data accumulation.


Supermarkets are combining their loyalty card data with social media information to detect and leverage changing buying patterns. For example, it is easy for retailers to predict that a woman is pregnant simply based on the changing buying patterns. This allows them to target pregnant women with promotions for baby related goods.


Grocery Data – Customers usually buy steak, and are now buying hamburgers. They are also now buying with more coupons….This says a lot! Data can be very meaningful if used smartly! Collect data wisely, collect data when you have a good idea why you are collecting it and how you will use it. Using this data and combining insights can be very powerful. Using the above information, a retailer will be aware that the economy is in a down market and this will alert him to allow more promotions on steaks to boost the sales of steaks and to reduce the amount of steaks on the shelf until the economy is up again.


Many businesses collect data and do absolutely nothing with it, except store it! The data does not actually inform the business. Others, spend lots of time collecting the data, cleaning the data, and then produce beautiful charts and summary statistic tables for their regular reports and or dashboards, but that is as far as it goes…  If sales has dropped from one month to another, then the dashboard will display the drop using a bar chart or trend graph. But that is not good enough. The business should ask itself many more questions. Questions such as: Why has the sales dropped? Which products have dropped in sales? Which sales people have dropped in their sales? Which stores have dropped in sales? Which regions have dropped in sales? Which sales channel has dropped in sales?  And most importantly, what should the business do???


By answering the above series of questions, the business will find insights/golden nuggets that will inform the business as to the cause in the drop in sales. Based on these causes, the business will have to take the necessary steps to bring back the sales to an acceptable or increasing level.


Businesses need to develop dashboards that help them in their day to day business decisions. Always let the data speak to you, let it tell you what to do. Gone are the days when decisions were randomly made without any evidence or data!


Do let me know if you have any questions regarding the collection of data. Why do you collect it? And, when and how do you use your data.


What Makes Big Data Challenging?

Almost certainly, data is everywhere…everyday we ourselves are generating huge amounts of information: when we email, or  click on “like” on facebook, or “click on follow” on LinkedIn or tweet when we like a comment on Twitter, etc.

The types of data that we are generating has also changed. We now watch and share more “Youtube” videos on the go, or download and share more music on the go, or post more photos on the go. This results in most data generated being unstructured and also the speed at which we received it is much faster than a few years ago. In fact, 80% of the new data growth is of the form of videos, audio, photos, text, images, etc and millions of it is generated every second.


What makes Big Data even more challenging are the types of communications we use on social media, such as hashtags, colloquial speech, pictures, abbreviations and typos, etc. What do businesses do with the data? Collecting lots of data is easy….but applying analytical techniques to better make sense of the data, is not so easy!


Data is flowing in from all sorts of directions….blogs, websites, sensors, surveys, forums, etc….How do we make sense of all the data??? It seems the best thing to do, is to find out what works for you. What are your business problems or pain points? How can you use just the data that is related to your business problems or pain points? It is all about collecting data smartly, collecting on the data that will add value to your business!

Your Customer is in the Driving Seat….Join them on their Drive

Easy access to data & technology – puts your customer firmly in the driving seat. Your customer now makes decisions based on:
1. Online Reviews
2. Online Ratings
3. Social Media
And it’s Customer Analytics that can drive & enhance your customers’ experience by:
1. Providing insights about your customers.
2. Scoring your customers based on their buying behaviour, online discussions, likes & dislikes.
3. Making targeted recommendations to individual customers instead of Mass Marketing.
4. Providing opportunity for Proactive, Innovative Decision Making

Customer Analytics is the key to improving your customers’ experience with you. And, to enhance your customers’ experience with you, it starts with data!

Customer Analytics is essentially concerned with analyzing data and requires statistical analysis, data mining applications & machine learning algorithms.
By focusing on specific enhancements for your customers’ experience with you, you need to get the right people involved & use only the relevant data.
You also need to ensure your organisation has an online voice, promoting community conversations & keeping your customers engaged.
To keep your customers engaged, you need to provide them with some interesting information & knowledge about your products and services.

Analyse what customers are saying about your products & services. Derive actionable insights from you social media data & creatively decide to ‘WOW’ your
customer & allow your customer to enjoy their experience with you. Measure the impact!

Grow your customer base & strive for real time service, to keep them coming back for more!

But How do we Segment our Customers?

How should you segment your customers? The first question I would ask, what is your business objective?

Do you want to understand when to promote to a customer? If your answer is yes, then I would recommend studying your customer behaviour.

What do I mean by customer behaviour? We have to acknowledge that not all customers are the same. Some customers buy more often than others, some buy more consistently than others, some need a ‘push’ to buy, some will not buy no matter what you do…

So we need to understand our customers in terms of…
1. Which customers will buy
2. Which customers need a ‘push’ to buy
3. Which customers, no matter what you do, will not buy

There are various customer metrics that can help you to understand your customer behaviour. Two top of mind metrics for better understanding customer behaviour is:
1. RFM (Recency, Frequency, Monetary Value)
2. Latency (The Average Time between Purchases)

These two metrics are extremely good as they are actionable metrics…they indicate to you WHEN TO ACT. Very important!
Never compute metrics that do not indicate to you when to ACT or DO NOT assist you in making proactive decisions.

Today, I will speak a little on RFM. RFM allows you to score every customer based on:
1. When last they bought from you (Recency)
2. How often they bought from you (Frequency)
3. How much they have spent from you (Monetary)

Each customer, is scored on a rating from 1-5 for each metric Recency, Frequency, Monetary Value, where ‘5’ is most favourable and ‘1’ is least favourable.

The customers with the highest scores, for example, ‘555’ are your most valuable/profitable customers (They have bought most recent, most frequent and spent the most with you). These customers are likely to buy again on their own and do not need a ‘push’. They are probably your loyal customers. Customers who are already in love with your products. Pampers these customers.

The customers who were scored previously, for example, ‘111’ and now are scored ‘333’ are probably fairly new customers, you need to give them a ‘push’, promote to them through a ‘Cross Sell’ or ‘Up Sell’ Campaign. These customers may not be familiar with all your products as they are fairly new customers so by using ‘Cross-sell’ or ‘Up-Sell’ Campaigns, you are likely to get them to spend more with you & increase their likelihood of becoming loyal customers.

On the other hand, customers who have been previously scored, for example, ‘333’ and are now being scored a ‘111’, it is likely that these customers are about to leave you. Promote to these customers as this is where you are able to get high impact ROI, help these customers to spend more with you before they leave you. Send these customers a campaign, probably an anti-defection campaign.

Customers who have previously been scored ‘111’ whom you have promoted to since, and they still have not bought, these customers are likely to have left you already. Do not promote to these customers.

Does the above make sense? Let me know….If you have any questions, please feel free to ask them

Analysing your Customer Data…

Data is the new ammunition to conquer & prosper!

Every organisation can explore their customer data to determine how best to transform it in a way to create actionable insights that can support and direct the organisation’s marketing strategy and decision making…

Customer segmentation is the starting point as not all customers are the same. Different segments of your customers have different needs and wants. It is important for an organisation to understand their customer’s needs and wants so that they can segment their customers according to their needs and wants and thereby use target marketing, which will improve the return on investment as the response rate is likely to be much higher compared to mass marketing.

Of course, before we even start to segment our customer’s, we must ensure that the data is of good quality as ‘Garbage in, Garbage out’ (GIGO). We also need to know the current status in terms of our customer behaviour, how frequent do they buy, how much money do they usually spend, what is our usual response rates to our mass marketing campaigns, etc. The reason why this is important, is because we need to have a baseline (benchmark) so that once we start analysing our customer data, and using it for decision making, we need to know whether we are improving in response rates, improving in return on investment, customers are buying more often and spending more, etc.

After about 3 months, we need to compare our baseline metrics with our new targeted marketing response results to know whether
our customer segments and data analysis are delivering results. Communicate these quick wins to the rest of the organisation and continue to deliver results every week after that.

Does your organisation analyse its customer data? Do they obtain actionable insights from their customer data analysis? Are the right techniques being used? Share with me…

What is Customer Analytics?

Customer Analytics is about analysing customer data to better understand your customer behaviour, needs & wants. But who exactly are your customers. Are customers who last bought 5 years ago, still your customer? Organisations need to firstly define, what are they talking about, when they talk about ‘customer’ so that the whole organisation is using the same definition and there is consistency within the organisation.

It is also very important for organisations to understand who their profitable customers are. As the rule of thumb ‘20% of customers bring in 80% of your revenue’. So the starting point in customer analytics is to identify who are your profitable & valuable customers. It is not just looking at which customers have spent the most with you, as some customers have been with the organisation for a longer period than others. It’s about identifying for each customer, when last did they buy, how often do they buy, how much have they spent with you in a specified period of time? Based on these results, each customer is scored and the top 20% or so will clearly be identified & confirmed as the organisations profitable & valuable customers.

Do you know your organisations definition of a ‘Customer’?
Will all the employees of your organisation, if asked who are your organisations customers, compute the same answer?
Do you know who are your organisations top 10 profitable & valuable customers?

It’s time to know your customers….this is just the start!

Dr Carol Anne Hargreaves of NUS talk about big data and Trade Index

Dr Carol Anne Hargreaves of NUS talk about big data and Trade Index

The Trade Index which we are developing, will provide quantitative insights into Singapore’s import and export trends. With the Trade Index, we will able to tell, which country is contributing the most to imports; the government will then know which country is driving the export or likewise the import. The government can then make decisions whether it should release more resources, or whether it should  exercise restrain, whether the industry is in good or bad health… The Trade Index will help to make recommendations to drive the market from red to green. When combined with client data through statistically significant quantitative models, these insights will enable local corporations to proactively predict sector specific consumption with greater accuracy. This will lead to significantly improved operational and strategic decisions in key areas such as sourcing, inventory management, distribution and allocation of working capital investments.

The 7-Step Business Analytics Process

The 7-Step Business Analytics ProcessThe 7-step Business Analytics Process

Real-time analysis is an emerging business tool that is changing the traditional ways enterprises do business. More and more organisations are today exploiting business analytics to enable proactive decision making; in other words, they are switching from reacting to situations to anticipating them.

One of the reasons for the flourishing of business analytics as a tool is that it can be applied in any industry where data is captured and accessible. This data can be used for a variety of reasons, ranging from improving customer service as well improving the organisation’s capability to predict fraud to offering valuable insights on online and digital information.

However business analytics is applied, the key outcome is the same: The solving of business problems using the relevant data and turning it into insights, providing the enterprise with the knowledge it needs to proactively make decisions. In this way the enterprise will gain a competitive advantage in the marketplace.
So what is business analytics? Essentially, business analytics is a 7-step process, outlined below.

Step 1. Defining the business needs
The first stage in the business analytics process involves understanding what the business would like to improve on or the problem it wants solved. Sometimes, the goal is broken down into smaller goals. Relevant data needed to solve these business goals are decided upon by the business stakeholders, business users with the domain knowledge and the business analyst. At this stage, key questions such as, “what data is available”, “how can we use it”, “do we have sufficient data” must be answered.

Step 2. Explore the data
This stage involves cleaning the data, making computations for missing data, removing outliers, and transforming combinations of variables to form new variables. Time series graphs are plotted as they are able to indicate any patterns or outliers. The removal of outliers from the dataset is a very important task as outliers often affect the accuracy of the model if they are allowed to remain in the data set. As the saying goes: Garbage in, garbage out (GIGO)!

Once the data has been cleaned, the analyst will try to make better sense of the data. The analyst will plot the data using scatter plots (to identify possible correlation or non-linearity). He will visually check all possible slices of data and summarise the data using appropriate visualisation and descriptive statistics (such as mean, standard deviation, range, mode, median) that will help provide a basic understanding of the data. At this stage, the analyst is already looking for general patterns and actionable insights that can be derived to achieve the business goal.

Step 3. Analyse the data
At this stage, using statistical analysis methods such as correlation analysis and hypothesis testing, the analyst will find all factors that are related to the target variable. The analyst will also perform simple regression analysis to see whether simple predictions can be made. In addition, different groups are compared using different assumptions and these are tested using hypothesis testing. Often, it is at this stage that the data is cut, sliced and diced and different comparisons are made while trying to derive actionable insights from the data.

Step 4. Predict what is likely to happen
Business analytics is about being proactive in decision making. At this stage, the analyst will model the data using predictive techniques that include decision trees, neural networks and logistic regression. These techniques will uncover insights and patterns that highlight relationships and ‘hidden evidences’ of the most influential variables. The analyst will then compare the predictive values with the actual values and compute the predictive errors. Usually, several predictive models are ran and the best performing model selected based on model accuracy and outcomes.

Step 5. Optimise (find the best solution)
At this stage the analyst will apply the predictive model coefficients and outcomes to run ‘what-if’ scenarios, using targets set by managers to determine the best solution, with the given constraints and limitations. The analyst will select the optimal solution and model based on the lowest error, management targets and his intuitive recognition of the model coefficients that are most aligned to the organisation’s strategic goal.

Step 6. Make a decision and measure the outcome
The analyst will then make decisions and take action based on the derived insights from the model and the organisational goals. An appropriate period of time after this action has been taken, the outcome of the action is then measured.

Step 7. Update the system with the results of the decision
Finally the results of the decision and action and the new insights derived from the model are recorded and updated into the database. Information such as, ‘was the decision and action effective?’, ‘how did the treatment group compare with the control group?’ and ‘what was the return on investment?’ are uploaded into the database. The result is an evolving database that is continuously updated as soon as new insights and knowledge are derived.

Challenges in Business Analytics

Today, data is everywhere (internet, emails, sms, linkedin, twitter, facebook, online magazines, online newspapers, online journals, etc) and easily accessible and the result is ‘Big Data’ (data in a variety of forms, of different sizes and complexities). With many trillions of click stream records being generated every second, and aggregated to records with thousands of attributes, massive data and databases are being built. But how do we make sense of the massive data? There is a clear need and increasing demand for business analytics techniques to find patterns in the data and to present these in a simple way and at the same time display insights.

One of the challenges today is, in the past, business users relied on statisticians to analyze the data and to report the results. And, the results typically raised more questions and generally it took a long while, sometimes months, before business users could actually act on the results. Business users, while being domain knowledge experts in their particular areas, are not likely to be experts in statistics and business analytics.

Business users are also placing more demands on IT for more information in near real time. For organizations to better understand and improve the results of their business processes along one or more dimensions, organizations need data science teams which consist of experts in business analytics, domain knowledge and IT. Another key challenge for organizations is the transition of analytics onto mobile devices. Leading organizations have started to store data for mobile distribution.

With the growing variety in data, organization’s need to determine the types of data they want to analyze. It is also critical for organization’s to be able to leverage on both structured & unstructured data.  The major challenge that organization’s face in today’s competitive environment is to identify which data is relevant and useful and then to transform the data into useful knowledge for business decisions.

Organizations need to have tools or roadmaps to put their analysis results and insights into use for actionable decision making. Organizations also need to share information throughout their enterprise, so that analytics can be used in a proactive way, to grow profits and streamline operations in the hope of gaining a competitive advantage over their competitors.

Is there a difference between Business Analytics, Business Intelligence & Statistical Analysis?

Today, businesses are being challenged by the amount of data they have, as they don’t quite know what to do with the data. Further, there is a short supply of skilled business analytic talent.
Recently, there has been an increased demand for Business Analytics, and this will continue to rise. Many today are also confused about the difference between Business Aanlytics, Business Intelligence and Statistical Analysis.
Business analytics is all about turning data into information and information into knowledge thus helping a business to take the right decisions. Business Analytics encompasses 5 key areas:
1. Business analytics is all about what is likely to happen in the future.(Prediction)
2. Business analytics is all about using historical data to forecast future values (Forecasting)
3. What is the worst that can happen? What is the best that can happen? (Simulation)
4. How should we take action? How will we measure the impact of our actions? (Optimisation)


 5. Which action will result in the most ROI? (Optimisation)

While Business Intelligence (BI), is about providing a platform that displays historical and current views of  the business operations in the form of  cubes, reports, dashboards, etc.

Statistical Analysis are the methods applied to business problems to make sense of the data. Statistical analysis is all about better understanding your data using graphical displays and summary statistics. It is useful for making comparisons between two or more groups and determining whether one variable is related to another. Statistical analysis also helps to identify drivers of performance.