A regular dose of D2C-centric resources & tools for Growing Brands, Startups & Entrepreneurs.

Most of you might be familiar with the butterfly effect, the behaviour described by Edward Lorenz—a mathematician, meteorologist, and the father of Chaos Theory…

For those who aren’t, the simplest explanation is this: small changes can have large impacts.

And we are here to explain how things that may appear trivial, can affect complex systems and result in a large impact for D2C brands - i.e., The Butterfly Effect of Data on Customers!

Enabling brands with access to high-quality data is important for business success. But in today’s advanced digital world, it is easy to gather data, but not utilise it.

You can have a ‘Lake of Data’, but you need only a ‘Warehouse of Data’ to access what you need and whenever you need it.

Data Lakes are just a dump of large volumes of raw data, while Data Warehouses focus on structured, processed data optimised for querying and analysis.

And unfortunately, brands have 1 or more services in place across each stage of the customer journey to maximise customer experience (CX).

Meaning, brands can’t possibly -

1. Gather the data for each of those services

2. View the raw data from all those services

3. And also analyse them

To conclude, if brands can get hands on someone that can provide them with a warehouse of data, (say from 450+ brands) they should capitalise on it.

3 roles played by an ideal Data Warehouse

Various stages of customer journey requires different forms of data representation or analysis – and they are to be Data-driven, Data-informed, and Data-inspired

1. Data-driven: you have the exact data you need to make a decision. If you are being data-driven, you agree with this statement, “It will tell you exactly the answer you need to know in terms of what to do next”.
   
   Example: the historic data to optimise marketing campaigns

2. Data-informed: means everyone is aware of the current performance and why the product is performing the way it is in order to make optimizations to your strategies.
   
   Example: the real-time ecommerce dashboard to keep track

3. Data-inspired: refers to trendspotting. This takes a few different data sources to put the story together since predicting future customer expectations is difficult to do with one data source.
   
   Example: data from similar brands to influence optimisation

The Butterfly Effect of Data on Services

According to a survey conducted by Salesforce, 72% of consumers expect companies to understand their needs and provide personalised experiences. Furthermore, 84% of customers reported that being treated as an individual, rather than a number, influences their loyalty.

Meaning, a small improvement in services across stages, will impact greatly in terms of retention and customer satisfaction.

Which is exactly why personalised product recommendations accounted for an average of 26% of e-commerce revenue.

1. Marketing & Data

1. Have access to data on individual customers from across customer journey stages, platforms, and brands

2. Message individuals on channel of preference

3. Understand the OR, CTR, ROAS etc of various campaigns

4. Optimise campaigns based on success rate

5. Increase conversions by 5-10%

📝 Fact 1: A study by McKinsey found that companies that leverage customer behaviour data to generate insights and target their messaging outperform their competitors by 85% in sales growth and more than 25% in gross margin.

📝 Fact 2: According to a study by Accenture, 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations.

2. Abandoned Carts & Data

1. Have access to data on where drop-offs occur

2. Share real-time data with logistic partners, consumers, customer support etc. Ensure everyone is informed at all times

3. Message individuals with incentives on channel of preference

4. Increase abandoned cart recovery by 15-20%

📝 Fact: A study by Deloitte found that personalised loyalty rewards based on customer data can increase customer spending by 20% or more.

3. RTO & Data

1. Have access to data on fraudulent users, frequent return users etc. from 450+ brands

2. Get another confirmation after order is placed (on customer preferred channels), when it comes to -

   1. Locations that cost more logistically

   2. Pin codes with high NDR rates

   3. Impulsive orders

3. Disable COD option for high risk users during checkout

4. Reduce RTOs by >60%

📝 Fact: Return-to-Origin orders are the highest in India because we also have the highest influx of COD order - with 16% of global RTOs coming from Indian COD orders alone.

4. Return Management & Data

1. Have access to historic returns data from customers, across brands

2. Understand the most frequent causes behind Returns & NDRs

3. Optimise communication between: brand ↔️ logistics partner ↔️ consumer

4. Reduce NDR by >20%

5. Improve customer satisfaction by >15%

📝 Fact: According to a study by Aberdeen Group, companies that leverage customer analytics are 6.4 times more likely to resolve customer issues before they become problems.

That’s the end of our talk on “The 🦋 Butterfly Effect of Data on Customers”...

☕ See you on the next coffee date!

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