You want to join the competitors and start analysing your data, but think it’s too late to ask the basic questions? Worry not, this short compendium will guide you through the basics of data adoption for your company.
By now, you can be sure that data analytics is not a fad. It was tried, tested and proven. It is not a passing trend, but a necessity for enterprises hoping to stay afloat in the years to come.
You want to join the competitors and start analysing your data, but think it’s too late to ask the basic questions? Worry not, this short compendium will guide you through the basics of data adoption for your company.
By now, you can be sure that data analytics is not a fad. It was tried, tested and proven. It is not a passing trend, but a necessity for enterprises hoping to stay afloat in the years to come.
Cost, security and infrastructure are no longer obstacles for adopting data analytics.
The cost of running an analytics project is a fraction of what it was just a few years ago. There are hundreds of analytic companies competing for your business.
Security lies at the heart of the structure of analytic vendors, as your data and its safe travel are their livelihoods. Analytics providers seldom keep data for a period extending the time necessary to run the research. And if you choose the right supplier, that time will be short.
Infrastructure? Customers spoke, and industry listened. Infrastructure obstacle is a thing of the past. Look up – it’s all in the cloud! Vendors handle the technical aspects while a new breed of Over The Top solutions integrates seamlessly with your existing systems.
What’s more – with the diversification in the market, there is more than just one answer to your data needs. Vendors specialise in dedicated areas of analysis designed to solve specific problems. Rather than having to submerge yourself in the abyss of big data, you can test the waters with a tailored solution. For example, Conversation Analytics focusses on the phone conversations to reduce the risk of fraud, improve the quality of customer service, and enhance the Voice of the Customer program.
So why are 62% of Australian companies planning, yet not quite using big data? It is the lack of the simple ‘know-how’.
Here’s a 101 crash course to doing big data analytics:
1. LET THE RIGHT ONE IN
How to chose your vendor? Here are a few pointers:
– Transparency
If your prospective vendor cannot answer the question of who will have access to your data, run for the hills. Security is a key, and a vendor who cannot succinctly explain who handles your information is a no-go.
– Ease of implementation
Most vendors will be happy to offer you a tech-analytics-report package./ If your company does not have an internal analytics department, look for a partner who can provide you with findings in a clear, comprehensible form.
– Speed
Pick a vendor who offers real-time or close to real-time analytics. While you may not use this option during your first analytic approach, as you progress you will find that having a fast process-insight loop allows you to be more agile and cut down on the possible losses.
– Cost
Rather than plunging head first into a multi-million dollar investment, try dipping your toes in analytical waters before committing to one vendor. Don’t settle for the first Google result. Run sample tests before you decide. Let the points above guide you on your quest. Scroll down for Calculating the ROI part.
2. POP THE QUESTION
If it so happens that you have a philanthropic spirit and a huge pile of money lying around, by all means, please just ask for ‘the insights’. There is always a chance that you will receive some general wisdom in return for your spare dollar. If, however, you are interested in gaining a real advantage, ASK THE QUESTION: WHAT PROBLEM AM I TRYING TO SOLVE?
“Part of the problem with big data is that we have become so enamored with the technology, we’ve forgotten what business problems we’re trying to solve with it.”
Claudia Imhoff, CEO of Intelligent Solutions
Data analytics needs a task. Give it an ambiguous assignment and expect an equally unclear answer. To gain most from your analytic endeavor consider what knowledge would help you to perform better.
Call Journey’s area of expertise is Conversation Analytics. We usually work with companies interested in increasing customer satisfaction or employee compliance, and reducing customer and agent attrition. Here are some examples of the question our research helps to answer:
Simple queries:
Why are customers calling?
What are customers angry about?
What do they think of our company?
Correlations:
Where are angry customers calling from?
What emotion produces most upsell opportunities for our product?
What customer-agent matching (age, gender) results in the highest satisfaction levels?
Which part of the script causes customer’s / agent’s irritation?
Pick a query that translates directly into advancing your enterprise.
3. BUILD A BUSINESS CASE
Calculating the ROI on analytics is no different from calculating the ROI on a new campaign or new product (although it comes with an asterisk; *implementing the findings is required). Although research projects tend to yield unexpected gains, when preparing a business case focus on the task, and not on the possible ‘ifs’.
Example:
You want to reduce the customer churn by 2% by segmenting the most disgruntled customers. You have tasked your analytics project with finding the angriest conversations (among the many thousands of calls) and segmenting them by topic, demographics and location. Segmenting in this way will allow you to focus on the high-risk customers and address their specific needs, thus reducing the churn.
Building a business case should start with determining the gain this process will yield. Each basis point reduction in churn will translate into a “total value of customers saved” metric. This is a tangible number that will give you a basis for calculating your analytics ROI.
And then there are the perks:
– Finding out what makes customers angry (and, if possible, removing the problem for good)
– Consequently, limiting the volume of calls
– Increasing NPS score
– Discovering insights that lead to the creation of additional revenue sources (listen and thou shalt receive)
These are the extras that may be difficult to include in a ROI calculation. Use them to cause delight among executives after the completion. In culinary terms, it’s called “the dessert”.
4. ANALYTICS IS NOT A HOT DOG EATING CONTEST
When it comes to data analytics, the scale is not the priority. It’s the relevance that matters. Don’t start off with a multi-million dollar contract that will shower you with metrics you won’t be able to use. Start small and work up. Treat your first project as an exercise. Go with a vendor who offers an introductory program for a fraction of a cost. Adress one issue and nominate the ‘implementation team’. Set your expectations. What will you consider a success?
Make sure to run a simple checklist:
Before:
– Who will take ownership of new information?
– Who will have access to the results?
– Who will handle implementing the findings?
After:
– How did the analytic vendor/partner perform?
– Were there any collaboration problems that have to be addressed?
– Did the research answer my question?
– Were findings presented in digestible format or form?
– How long did implementing changes take?
– What was the benefit?
“Over half of the committed executives say that big data analytics has increased revenue by more than 3%. Over 90% say it has increased revenue by more than 1%.” Forbes and Teradata 2015 Survey
With the analytics industry booming and the technology behind it becoming increasingly sophisticated, yet easier to implement, it becomes more and more difficult to design an analytics process that will end up as a flop. Adopt the basic rules and tap into the source that can become the ace up your enterprise’s sleeve for years to come.
