You need to truly understand your own service and product offerings when evaluating your strategy. You may not understand them as well as you think, and your customers may perceive them in completely different ways than you’d expect. What is working and what is not working? How are customers responding to your products? Where are you losing money through inefficiencies?
If your web offering is a significant part of your business, find out what is
working there and work to make it better. Create and track micro-conversions to see how your items are performing even before a purchase is made. These
provide valuable insights even if they are not part of a funnel analysis.
Track the customer engagement with your other digital offerings.
What is the applause rate on your social media? (How many times are your tweets liked or retweeted? How many comments on your Facebook posts?) How many people download your material?
How many people sign up for your newsletter?
Test changes in your products by running A/B tests, which you’ll do in the following way:
1. Propose one small change that you think may improve your offering.
Change one frame, one phrase, or one banner. Check with your development team to make sure it’s an easy change.
2. Decide what key performance indicators (KPI) you most want to increase:
revenue, purchases, up-sells, time onsite, etc. Monitor the impact on other KPIs.
3. Run the original and the changed version (A and B) simultaneously. For websites, use an A/B tool such as Optimizely. View the results using the tool or place the test version ID in web tags and analyse specifics of each version, such as by comparing lengths of path to conversion.
4. Check if results are statistically significant using a two-sample hypothesis test. Have an analyst do this or use an on-line calculator such as
https://abtestguide.com/calc/.
5. Use your big data system for deeper analysis:
a. Were there significant changes in customer journey, such as number of
categories viewed or filters selected?
b. Are there key product or customer segments you should manage differently?
c. Did specific external events influence results?
d. Did KPIs move in different directions?
Align your assumptions about your product with these new insights. For example:
Are you trying to compete on price, while most of your revenue is coming from customers who are quality conscious?
Are you not taking time to curate customer reviews, while most of your customers are actively sorting on and reading those reviews?
If your assumptions about your product don’t align with what you learn about your customers’ preferences and habits, it may be time for a strategic pivot.
Use modern data and data science (analytics) to get the insights you’ll need to determine and refine your strategy. Selectively choose the areas in which you should focus your efforts in (big) data and data science and then determine the necessary tools, teams and processes.
In the next chapter, I’ll talk about how to choose and prioritize your data efforts.
Takeaways
Big data sources will help inform your strategy by giving new insights into your customers, competition, business environment and product.
There are many new sources of non-traditional data. Take an inventory of what is available and what is most useful.
You will typically have difficulty linking your customer actions across different touch points.
Your website and other digital portals can provide detailed information about customer intentions, preferences and habits, signalling when you need to make a tactical change or a strategic pivot.
Running your A/B tests in combination with a big data system allows you to gather much deeper insights.
Ask yourself
Make a list of your customer touchpoints. For each, note if you are digitalizing and storing none, some, or all the available data. Rate each
touchpoint from 1 to 10 in terms of (a) the value of the data and (b) how difficult it would be to store and analyse additional data from that
touchpoint. Multiply those two scores together. The touchpoints with the highest resulting numbers are your most promising new sources of customer data.
What data sources would you need to link to get a full view of your customer interactions? Is there anything preventing you from linking this data?
What are ways in which your customers differ in their preferences and behaviour and which might impact the way in which you relate to them and the products and experiences you offer to them?
What have been the most successful product changes that you made after testing the results of different possibilities?
Which external data is relevant for your organization: economic data, weather, holiday schedules, etc.?
Which data sources could give you more insight into your competition?
Think of private and public information providers as well as graphs and signals provided by internet companies such as Google and LinkedIn.
Chapter 7
Forming your strategy for big data and data science
‘… let’s seek to understand how the new generation of technology companies are doing what they do, what the broader consequences are for
businesses and the economy …’ —Marc Andreessen.54
It’s exciting for me to sit with business leaders to explore ways in which data and analytics can solve their challenges and open new possibilities. From my
experience, there are different paths that lead a company to the point where they are ready to take a significant step forward in their use of data and analytics.
Companies that have always operated with a minimal use of data may have been suddenly blindsided by a crisis or may be growing exasperated by:
lagged or inaccurate reporting;
wasted marketing spend;
time lost to poor sales leads;
wasted inventory; or
any one of a host of operational handicaps that can result when data is ignored or data solutions are constructed in a short-sighted manner.
They end up forced to run damage control in these areas, but are ultimately seeking to improve operations at a fundamental level and lay the groundwork for future growth.
Companies that have been operating with a data-driven mindset may be exploring innovative ways to grow their use of data and analytics. They are looking for new data sources and technologies that will give competitive advantages or are exploring ways to quickly scale up and optimize a proven product by applying advances in parallel computing, artificial intelligence and machine learning.
Regardless of which description best fits your company, the first step you’ll want to take when re-evaluating your use of data and analytics is to form a strong programme team.