Let’s talk about hippos. You didn’t expect that, did you?
A “HiPPO” is an acronym for the “highest paid person’s opinion”. It’s an old business model suggesting that key decisions should be made by the senior top paid professionals based on their gut feelings, opinions and experience.
In today’s data rich, digital environments, decision making made by “hippos” is a thing of the past. Instead, modern business decisions are driven by data analytics and insights.
Today, HiPPO has been replaced by another acronym, DDDM – data-driven decision making.
One of Une Amundsen’s (our founder’s) favorite quotes was “facts kill all discussions” and we truly believe it.
Yes, we are all entitled to an opinion, but it’s the numbers and data that drive our decision-making process. And we want to you show you how.
This is the story how DDDM helped us increase sales, improve product development and double down on customer centricity. We’ll also be sharing key results and lessons learned along the way.
Ready? Let's get started.
Making data work for you
Let me be blunt – the data, in and of itself, has no value and means nothing…
… unless you know how to use it.
For example, knowing that you sold your product to 10 companies this week, as compared to 15 companies the week before, won’t help you increase sales. But knowing what actions you’d taken that resulted in an increase in sales numbers – would!
In other words, DDDM heavily depends on the questions that you ask and the insights that you seek.
So, what kind of questions have we asked ourselves to increase sales, improve our customer-centric approach and improve our product – SuperOffice CRM – to help us find the insights to stimulate business growth?
How we use data to improve sales
Did you know that the sales teams that use sales analytics are three times more likely to outperform that those that do not?
In order to successfully run a sales team you need to work with data, i.e. define key metrics, set goals, measure input and output, and analyze the results.
Because sales isn’t about guessing. It’s about having a fact-based strategy.
And it’s by using facts that we’ve been able to increase average close rates by a staggering 50%!
How, you ask?
It started with a comparison between our sales reps that had high close rates versus those that had low close rates. What we found was that the most successful sales reps had an additional step in their sales process.
This additional step was something we could track inside SuperOffice CRM. You see – anytime we meet, speak or communicate (market) with a prospect, it’s recorded as a sales activity inside our CRM system. All of these activities add up to a specific number of touchpoints. In most cases, the activities were an exact match. Except one.
What we found out was that the most successful sales reps had an additional activity in their sales process. Rather than wait until the end of the sales process to get sign off, the most successful sales reps would ask for a sign off in the middle of the sales process.
Asking for commitment much earlier in the process meant the prospect was more likely to buy. It also meant that our sales reps could be 100% focused on those buyers who signed off midway, thus leading to higher close rates.
Since discovering this, we’ve made sure that our sales process now includes a sign off in the middle of the process for all of our sales reps, leading to a huge impact on our average close rate.
How we use data to improve customer centricity
We pride ourselves in being a customer-centric company.
Customer centricity for us means putting the customer first by listening to what they want, creating an enjoyable and valuable experience, and making sure they stay with us.
Did you know that 68% of customers stop doing business with you because they feel you do not care?
No one likes to feel neglected, so to make sure that no SuperOffice customer feels neglected, we use 2 powerful data points to monitor our customers’ well-being – orphans and heartbeat.
First, we use CRM data to identify “orphans” – i.e. customers who have not been followed up on during a specific period of time. Identifying orphans helps us take timely action, before it is too late.
Second, we track (of course, with our customers’ consent) whether or not they use our product at all.
We call it “heartbeat” because if our customers are using the software and logging in daily, they have an active pulse. If not, they’re flatlining, and well, you know what that means…
If the “heartbeat” is low and the customer is also an “orphan”, it’s a major warning sign that the customer is heading for the door.
But, we want to be proactive and not wait until they leave before we try to speak with them.
Should any of the two happen, our dedicated Customer Success team is instantly alerted, so that they can take action and reactive the dormant customer.
They reach out, speak with and invite the customer to upcoming events, seminars or webinars or help them find value by offering on the spot training. We also come by to their office to walk them and their team through the product to remind them why they chose it in the first place.
But does it work?
You bet it does!
As a result of these efforts, our churn rate has gone down by 4.02% within 12 months, representing a significant improvement to our ROI!
How we use data to improve product development
When it comes to product development, we always rely on data.
The new features and product development areas that our developer team is engaged with come from a variety of sources: the Net Promoter Score (NPS), internal and partner feedback, customer service enquiries, and, of course, product usage.
Product usage is one of the most important metrics for any SaaS business success.
No surprise – we are extremely curious to find out whether or not our customers use our products. And thanks to today’s new, non-intrusive technologies, capturing and processing this kind of data is easy.
What parts of our product do customers use the most? And are there features that are rarely or never used?
If a feature has little or no use, then it is important to investigate why. Did it miss the mark, is it too complicated, or maybe it’s just redundant? And vice versa – is our Minimum Viable Product (MVP) gaining traction? Should we expand and improve it?
It’s the latter which has helped us begin work on rolling out SuperOffice Chat 2.0.
In May 2017, we launched SuperOffice Chat – the first of a kind chat solution that is directly connected to a CRM solution. Chat is complicated. Not the software, but rolling out a live chat feature company-wide isn’t a straightforward endeavor. If you launch chat on your website, you need to be available for customers when they use it. This means changing work schedules and routines to accommodate it.
So, we built our chat MVP, launched it and started work on our GDPR-specific functionality.
But, here’s the thing:
Since launching Chat to our customer base (for free, by the way), our customers have handled more than 140,000 chat sessions with their own customers. That’s a lot of real-time conversations and because of it, our customers have been able to provide a better experience for their own customer base.
The success of SuperOffice Chat prompted that we need to up the game.
Product usage is growing, and we can see a clear trend (up, and to the right) of the companies that are using SuperOffice Chat in their business. And this is exactly why we’re launching SuperOffice Chat 2.0 to include new features and functionality. If the chat usage trend continues, then you can expect us to begin work on SuperOffice Chat 3.0 in the near future!
How to succeed with DDDM
Looking back to our own experience with using data analytics to support our business decisions, we’d like to share a few observations on how to apply the DDDM method in your business:
1. Frame the questions right
Fundamental to all analytics is the need to know what we want insights on – what questions to ask, what KPIs to track, etc. This all defines what data we need to look at and what analytical models and tools to use. The best advice is to start every new project by asking yourself what you want to achieve and how you’ll measure it.
2. Identify and organize your data sources
Once you have your questions or goals defined, move on to identifying your data sources and data models. What you need is a reliable, credible and customizable source. Stay away from the data sources that depend on manual input and prioritize the automatized ones.
3. Make the data insights known to all
Data can help your business in various ways: it can influence big strategic decisions or define your day-to-day operations. Whereas the former can remain in the executive board rooms, the latter type of data needs to be available to everyone, especially the customer-facing employees. If people know that the key decisions are based on facts and figures, their motivation to do the right thing increases and helps them act upon the data insights confidently.
4. Revisit and evolve your questions and research models
Naturally, things will change over time. Your customers and your goals will become different over time. This will affect the questions that you ask and the insights that you seek. So, from time to time, you will need to re-evaluate whether the measures you use are still relevant and applicable.
5. Make sure you have permission to collect data
You cannot simply collect data any way you want these days. You need to respect the rights of individuals and seek permission to gather and store their data. As stipulated by the EU General Data Protection Regulation (GDPR), data collection should be conducted by following a strict set of rules. Data integrity is, therefore, an integral part of how data collection should be done, who is going to use the data insights and for what purposes.
So, no more “HiPPO”-like decisions made on half-truths, gut feelings or opinions!
All this that can be harmful and simply ineffective. Meanwhile, data-driven decision making is one of the the most reliable ways to grow.
The principle of using data to support decision making has been a core principle for our company since the day we were established.
We understood that facts not only kill all discussions, but also eliminate opinions, instincts and other irrationalities. Decisions based on data helped us come up with more concrete goals and better way to measure results.
We also learned that in order to succeed in data analytics and base your business decisions on facts – you need to know what questions to ask, what data to collect, what patterns to look at and how to use the insights you get.
Finally, data-driven decision-making is not “big-boss exclusive”. It’s universal and is highly suitable for big strategic decisions, and small, every-day choices alike.
For more best practice tips, please visit us our Resources section.