Data Driven Organizations
Fuelling Business Growth through Data
The value of data in decision making is known to every business owner and managers. Decisions fuelled by data insights have higher rates of success. And yet, SME business owners rarely extract the complete value from business data.
The most common reason for this under utilization of data is always the existence of the right quantity and quality of data. As a business coach, whenever I ask about the data that supports a decision, I witness huge gaps in collecting, analysing and then application of this data. So, let us break down the entire data cycle for a business.
Conscious Collection of the Right Data
Notice the word – “Conscious” The reason for this emphasis is data, like dust, gets collected without us doing anything specifically. This is especially true in today’ digital world. The Right Data needs to be consciously identified and the collection of this data needs to be consciously structured.Notice the word – “Right” – The reason for this emphasis is to identify the gaps between data required and data collected. These data gaps can be either known or unknown gaps. Often data is collected and then we try to make use of it. However, data gaps focus on data we do not have.To identify these gaps, we start with our strategies that are based on certain assumptions or calculations. We then work backwards to validify those assumptions or calculations with the data we have. This step invariably exposes the gaps and helps us put together a conscious data collection architecture.
Notice the word – “Conscious” The reason for this emphasis is data, like dust, gets collected without us doing anything specifically. This is especially true in today’ digital world. The Right Data needs to be consciously identified and the collection of this data needs to be consciously structured.Notice the word – “Right” – The reason for this emphasis is to identify the gaps between data required and data collected. These data gaps can be either known or unknown gaps. Often data is collected and then we try to make use of it. However, data gaps focus on data we do not have.To identify these gaps, we start with our strategies that are based on certain assumptions or calculations. We then work backwards to validify those assumptions or calculations with the data we have. This step invariably exposes the gaps and helps us put together a conscious data collection architecture.
Question for our readers- What are the data gaps in your organizations and how can you consciously bridge this gap?
Right Analysis of Collected Data
Not all data is equal. We need to slice up the data in multiple ways to draw out useful analysis. Again, the emphasis is the “Right” Analysis. This step always needs multiple view points, mainly because data can tell us different stories. For example, when data tells us that we have a 40% conversion rate, it also tells us that we have 60% dropout rate.The same data can be broken down to sub-levels such as conversion rates of different product offerings, at different geographies, by different sales teams, for different customer segments, at different purchase stages, through different channels. We also need to be conscious about outliers that can skew the data and lead us to wrong conclusions.
Not all data is equal. We need to slice up the data in multiple ways to draw out useful analysis. Again, the emphasis is the “Right” Analysis. This step always needs multiple view points, mainly because data can tell us different stories. For example, when data tells us that we have a 40% conversion rate, it also tells us that we have 60% dropout rate.The same data can be broken down to sub-levels such as conversion rates of different product offerings, at different geographies, by different sales teams, for different customer segments, at different purchase stages, through different channels. We also need to be conscious about outliers that can skew the data and lead us to wrong conclusions.
Question for our readers- How often do you analyse your collected data?
Application of Insights from Data
In-depth analysis of this data can tell us where to focus our efforts to generate the maximum business growth. Should we focus on specific products that have higher or lower conversion rates? Or should we focus on specific channels that are/ are not giving us better ROI’s. Or maybe we explore new market segments that have lesser competitors, rather than fight for wallet share in a crowded market!It is also prudent to set aside time and experienced resources to analysis and apply this consciously collected data. We need to regularly analyse this data and revalidate our organizational strategies that are based on them.
In-depth analysis of this data can tell us where to focus our efforts to generate the maximum business growth. Should we focus on specific products that have higher or lower conversion rates? Or should we focus on specific channels that are/ are not giving us better ROI’s. Or maybe we explore new market segments that have lesser competitors, rather than fight for wallet share in a crowded market!It is also prudent to set aside time and experienced resources to analysis and apply this consciously collected data. We need to regularly analyse this data and revalidate our organizational strategies that are based on them.
Question for our readers- Who has access to all this data and analysis reports? How do they use it?
Data in different Business Functions
Data in Marketing
Today’s digital-heavy marketing channels churn out a lot of data. Data collection is hence eased to a certain extent. But there is still work to be done.I like to start with data that helps us understand the customer. While most organizations may have the demographic data such as the age, gender, or income of the customer, it is the behavioural data that is often missing.
Today’s digital-heavy marketing channels churn out a lot of data. Data collection is hence eased to a certain extent. But there is still work to be done.I like to start with data that helps us understand the customer. While most organizations may have the demographic data such as the age, gender, or income of the customer, it is the behavioural data that is often missing.
Data such as, what are the pains of the customer? What gains do they covet?
Why do they buy from you or your competitors? Or even, why don’t they buy from you or your competitors? The more intimately a business knows their customers, the better their marketing and sales results.The different marketing channels used by an organization, need continuous analysis to evaluate the best performing channels and the optimum use of marketing budgets. Marketing ROIs are often most difficult to establish and hence a conscious collection of marketing data, right from the beginning is of prime importance. Marketing is not one size fits all.Each marketing channel will yield different results based on the purchase stage, the offering, the competitive landscape. With outsourced (digital) marketing, data becomes even more important to ensure that resources are utilized to meet set revenue, marketing, and branding goals. Examples of these goals can include, number of registration forms filled, number of downloads, number of call back requests etc.
Why do they buy from you or your competitors? Or even, why don’t they buy from you or your competitors? The more intimately a business knows their customers, the better their marketing and sales results.The different marketing channels used by an organization, need continuous analysis to evaluate the best performing channels and the optimum use of marketing budgets. Marketing ROIs are often most difficult to establish and hence a conscious collection of marketing data, right from the beginning is of prime importance. Marketing is not one size fits all.Each marketing channel will yield different results based on the purchase stage, the offering, the competitive landscape. With outsourced (digital) marketing, data becomes even more important to ensure that resources are utilized to meet set revenue, marketing, and branding goals. Examples of these goals can include, number of registration forms filled, number of downloads, number of call back requests etc.
Data in Sales
The most common type of sales data is the billing data. Most organizations collect this data easily. However how much of this data is studied and used? Are we slicing the data to uncover deeper insights? Are we applying these insights to question the current status quo? Back to our earlier example, when we analyse the 40% conversion rate and ask questions such as, why are only 40% of our leads converted? Which products/ markets are pulling us down? Which competitors are winning these deals? How do we compare with these competitors? Do we have any untapped competitive advantage? The second type of sales data is customer data. It is more than just information about which customer is buying what product/ service. This data refers to valuable information about the buying behaviour of your customers.
The most common type of sales data is the billing data. Most organizations collect this data easily. However how much of this data is studied and used? Are we slicing the data to uncover deeper insights? Are we applying these insights to question the current status quo? Back to our earlier example, when we analyse the 40% conversion rate and ask questions such as, why are only 40% of our leads converted? Which products/ markets are pulling us down? Which competitors are winning these deals? How do we compare with these competitors? Do we have any untapped competitive advantage? The second type of sales data is customer data. It is more than just information about which customer is buying what product/ service. This data refers to valuable information about the buying behaviour of your customers.
For e.g.
Who are the decisions makers in the customer’s organization that prefer to do business with you?
What percentage of the wallet share do you have vs your competition?
Why do these customers buy what they buy from you, and not the competition?
What are the buying cycles of the customer?This type of data is not easily available, but at the same time the sales teams can collect this information consciously. In-fact, most often than not the sales team will have all this information in their head but may not have them recorded in a centralized place that can be accessed later.The third type of useful data is the sales funnel data. We start with the identification of the distinct stages of the sales cycle, capture the conversion and drop out ratios at each stage, remove the outliers from this analysis to offset wrong averages and end with applying this staged data to make improvements at each stage. Businesses are quiet often surprised on the insights that we gain from this exercise. This results in rolling out simple improvements at each stage that snowball into larger impacts across the entire sales funnel.
Data in Customer Service
Customer Service is more than Customer Support! Businesses often miss out analysing the data lying with their customer service teams. This data pertains to the reasons of customer calls. Are customers reaching out for installation guidance? Or are they following up on delivery dates? How can the business facilitate these needs pro-actively?For example, many businesses now offer ‘unboxing’ videos that serve both as a marketing content and usage guidelines too. Simultaneously we can also further slice the customer service calls to reveal areas of improvement for the organization. For example, what products/ services generate most service traffic? Which geographies do customers login the most service calls? How long do we take to resolve each type of service calls?Question for our readers-
Customer Service is more than Customer Support! Businesses often miss out analysing the data lying with their customer service teams. This data pertains to the reasons of customer calls. Are customers reaching out for installation guidance? Or are they following up on delivery dates? How can the business facilitate these needs pro-actively?For example, many businesses now offer ‘unboxing’ videos that serve both as a marketing content and usage guidelines too. Simultaneously we can also further slice the customer service calls to reveal areas of improvement for the organization. For example, what products/ services generate most service traffic? Which geographies do customers login the most service calls? How long do we take to resolve each type of service calls?Question for our readers-
What data does your sales, marketing and customer service teams collect consciously?
How often do you review this data? Who reviews this data?
Do you interlink your sales, marketing, and customer service data insights?Other Functions
Other critical functions such as HR, production, logistics, finance, IT, governance, operations, quality etc. each derive immense value from data. I have covered only three of these functions here.
Other critical functions such as HR, production, logistics, finance, IT, governance, operations, quality etc. each derive immense value from data. I have covered only three of these functions here.
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