What’s the difference between real-time analytics and historical analytics?

What’s the difference between real-time analytics and historical analytics?

October 8, 2019
What’s the difference between real-time analytics and historical analytics?

We make choices all day, from the clothes we put on to what we’re having for dinner. In the workplace, the decisions we make for our team members trickle down to customers. Outfits and dinner are highly personal — it’s nobody’s business how many times this week you wore the same pants and ate mac and cheese. But when it comes to decisions that affect our teams, how do we know we’re making the right choice? Customer expectations are changing the way we understand contact centers, and the ones using stone-age software to make decisions on the fly are going to struggle in the age where customer experience is a key differentiator.

Enter real-time analytics!

What are real-time analytics?

Customer experience is the bottom line when it comes to decision making. When customers are waiting in backed-up queues, and support agents are struggling to stay afloat, having access to the most recent and relevant data can help you make decisions that make this better for agents and customers alike.

Real-time analytics are crucial to a modern support strategy. This looks like a live feed of all the necessary and important information on your dashboard, or can be broadcast for support agents so they can see where to allocate their time. In real-time, you can visualize spikes in calls, changing queues, and which channels need support.

Eckhart Tolle writes in The Power of Now, “Realize deeply that the present moment is all you have.” The best time to have a holistic view of your customer’s journey is now. The best time to be informed is now—especially when the present moment subverts our expectations. Even when we have access to data that shows us familiar patterns, the reality is that the fast-paced environment of customer contact centers means things can be unpredictable.

Examples of real-time analytics

Heading a support team is tough. Let’s say you’ve implemented messaging as a support channel as part of your omnichannel vision. In all the excitement, you’ve set up your agents to be ready for a flood of queries through their favourite messaging channel. What you didn’t anticipate was all of the people who are continuing to call. Don’t fret. You can see which queues are backed up. Your agents can see which queues are backed up and redistribute themselves accordingly.

Imagine your company released a product, but not all of your agents have received the proper training. In real-time, you can see where calls are being routed and use this information to redirect your best experts. Personalizing your customer interactions, with software the displays relevant customer and conversational data, leads to a better customer experience in an age where personalization matters.

Of course, we’re not using real-time data to make long term decisions. Historical analytics give us access to a wealth of information that can be used for long-range planning. It can help us see where we made poor choices in the past, but also where we succeeded.

What are historical analytics?

Historical analytics, which you are probably more familiar with, are slower to update. They might refresh daily or even hourly, giving you access to key metrics. Looking backwards, historical data informs our understanding of things like CSAT, FRTs, and can help develop agent training around points of weakness. Looking ahead, we can forecast when agents are needed around holidays, product drops, and marketing campaigns.

Historical analytics are better for forecasting, too. Unlike conversations and queues, your agents’ schedules don’t update in real time. Neither to product releases. When you’re scheduling agents around a drop, or a holiday — or building training around tickets that need a little more elbow grease, you’re not looking at your real-time dashboard to make these decisions.

Long range planning, looking at historical data and putting together the patterns, is like predicting the weather. While we understand generally, based on trends, how our climate is changing year to year, daily weather reports are ever-changing and unpredictable. Managing a support team is different. We can anticipate spikes in calls around product drops and marketing campaigns, but how can we be sure on an immediate level? Which channels today are receiving the most traction? Which teams?

Which is better?

Trick question! Everything has its time and place. Like peanut butter and pickles, both are absolutely necessary when it comes to making a decision sandwich. Historical analytics have proven to be a challenge when it comes to impromptu resource allocation. If they worked, we wouldn’t be clamouring for up to date data, building dashboards that refresh in real time.

There is great value in agile decision making. Similarly, there is great value in being present, existing in the moment, and having access to the most up-to-date information. Decision making on-the-fly can improve customer experience when the present situation isn’t what we planned for — and how often is that the case?

Basing your decisions with the help of software used by contact centers can feel like wrestling with dinosaurs. Digital transformation is hard. Making decisions is hard, too. It’s entirely possible, however, to make better, faster, and educated decisions with data you can access in real-time.

You can learn more about real-time reporting in Zendesk Explore.