Search and Discovery writer
Sorry, there is no results for this query
Something’s going wrong with your ecommerce site. A product has suddenly stopped selling, or you’ve gotten complaints on social media about the shopping process. As a result, you have a business decision (or several) to make in order to solve the problem.
Which business decision-making-process do you use?
If you picked C, yes. Of course it’s the data — the right data — that leads to better business decisions.
Some business leaders still do not gather data, instead making business decisions in old-fashioned way(s), such as by listening to their intuitive guidance as they think about what’s happened to a product line in the past. However, analyzing the current data and then making informed decisions is obviously the correct answer.
Collecting real-time facts, figures, and metrics, getting actionable insights based on that data, and responding appropriately is smart, less risky, and basically required if you want to stay in business.
So do we even need a whole blog post to illuminate why C. is the best answer? Maybe not, but since you’ve read this far, we’ll suggest some ways to help you excel at uncovering user intent and making smart decisions.
In the past, business folk would have been at the top of their game while, for instance, taking a vote with the stakeholders, as there was no science-based way of illuminating the right decision. Getting relevant data that could accurately inform a good decision wasn’t viable. Execs and employees alike had to look at whatever information they could stumble across — maybe months- or weeks-old data gathered through batch data processing, for instance — and then take a guess at the best course of action for the use case.
Sometimes it worked and sometimes it didn’t. If it worked, there might have been no way for the team to replicate it. If it didn’t, there was likely no way to figure out why, not to mention how to course correct.
Fortunately, accessing real-time data to inform business decisions and then expertly acting on it is something many companies can do well now. Thanks to AI and machine learning, It’s an entirely different playing field for businesses, particularly those operating in the digital sphere, such as with ecommerce sites.
With advanced analytics, companies can dig up much more information, and do it relatively instantaneously. Plus, if the guidance is clear, they can take constructive steps to respond to their customers’ needs in real time.
Data-driven decisions mean leveraging an abundance of recent raw data. Being able to see trends and patterns can make all the difference. For example, by matching real-time data to product supply, machine learning is changing the ways many online retailers make critical decisions about managing their inventory.
Data-driven decision making (DDDM for short), then, is the process of making business decisions that are informed and backed up by a sufficient amount of data. Preferably real-time data that’s fresh and relevant.
That’s it. Letting the data do the talking, listening to what it says, understanding how you can best leverage the information, and acting accordingly.
Things get a little dicey when the concept of speediness enters the data-retrieval equation.
When referring to response times, the trendy “real time” is thrown around loosely, whether a marketer is referring to as-it’s-happening time or referencing the less-exciting version, near real time. Hate to dampen anyone’s enthusiasm or the zeal of marketing people, but as far as we know, no information is truly provided in real, as it’s occurring, time. That’s because in a data-driven approach, data still must be collected and processed, albeit in milliseconds, but still, as time is passing.
So “near real time” is a more accurate (and less legally iffy) term. That doesn’t mean some software programs aren’t faster when it comes to data: what’s considered near-zero lag time varies with apps and websites, so some companies’ claims of real-time analysis are truer than others’. (At Algolia, we refer simply to instantaneous search results: no delay between a user’s typing a letter and getting a response.)
Real-time visibility of changing indications and environments lets companies gather valuable business intelligence, then quickly respond in ways likely to improve the customer experience or otherwise pay off. Predictive machine learning (ML) models can correlate a ton of data — and fast — to illuminate problems. That means a head start on addressing what needs to be overhauled. If your competitors are one step ahead with data-based operations and decisions while you’re relying on old information for your key decisions, they’re likely to pull even further ahead.
To a large degree, successful companies are taking advantage of data-driven technology to enhance their decision making and ultimately use the process to get a leg up on their competitors and meet their business goals. We know this because:
So there you have the going wisdom: it’s a great bet to use real-time analytics as the foundation for business decisions that can improve your key performance indicators (KPIs).
So what’s the hold up in terms of more businesses turning their data-driven nuggets into major successes? Sadly, it’s not necessarily as easy to spin knowledge — basic as it may seem and clear as it may look — into the silk of well-made real-time-analysis decisions.
Even with all the legitimate data you could drive into your organization’s collective field of vision, you still have to be able to make sense of it, to be able to extract the juicy parts from the drivel that keeps piling up. Data can also be overly complex, which can make its analysis a challenge.
Companies don’t necessarily know how to translate their comprehensive data into success; they don’t know the right methods for analyzing the reams of information they’ve dug up.
In fact, experts estimate that only a fraction of business leaders who are aware of the importance of using data constructively are able to harness real-time data to steer their business where it needs to go.
Successful data-driven decisions can’t necessarily be achieved by having the right tech gurus on staff to interpret the tea leaves, or even by selecting the right analytics technology provider. Good outcomes aren’t guaranteed.
Here’s a study confirming this unfortunate situation. The researchers cite:
Kinda sobering. But it doesn’t mean data-driven decisions should be abandoned altogether.
Despite the possible roadblocks, many companies go ahead and invest in analytics tools. And then, if they can beat the odds — look at their graphs and dashboards, figure out how to analyze the data, and do something great revenue-wise — they win.
Plus, one “consolation” benefit, if you can’t make the best data-driven decisions yet, is that some of those decisions can be made for you. Some types of human decision making are no longer needed because with AI, certain optimal decisions can be automated.
(Hey thanks, AI data analysts! Probably nobody has ever thanked you bots for your tireless information retrieval or ideas. Just remember: while we like what you do, you’re not in charge, you’re just making our decisions.)
Obstacles and bot appreciation aside, let’s focus on how applying real-time analytics can help you capture and optimize your site or app so you can better understand your user intent.
Thanks to accurately made data-based decisions, consumers have become used to having their needs instantaneously met, such as when they’re shopping online. If you can use real-time data-processing conclusions to cross-sell or upsell when someone is checking out on your site, for instance, you could realize a sizable increase in profit.
With smart data-driven decisions, you stand to:
Get valuable insights. When you can get to the bottom of your data, you’re halfway back up to the top.
See opportunities. With good data come solid ideas and direction.
Make better decisions based on proven, reliable data sets.
Strike while the iron is hot. With real-time analytics, you can learn about and then proactively address customer issues and changes in demand.
Improve efficiency. When executives, managers, employees, and consultants have all the data points they need to draw smart business conclusions, fewer risks may be taken. Fewer mistakes may be made, causing less wheel spinning and wasted time.
Satisfy customers and increase conversions. What’s at the root of effective customer service and personalization? Insightful analytics.
Beat your competition by discovering hidden gems of data you can use to pull ahead of rivals that may not be employing an analytics platform.
Create a data-driven culture. When you value and promote digital insight as an asset for team members and your organization, you create an education-centered culture.
Be more agile. Going forward with data as your guide, you can routinely make the right choices to keep your momentum going.
Increase revenue. Using data analytics to make the right decisions is beyond gratifying when it takes your earnings to significant new heights.
What are scenarios where key data-driven decision making is known to have played a key role in redirecting marketing efforts and being transformative for an enterprise? Here are a couple of inspirational big-business examples.
This megaretailer collects extensive customer data and uses it in various ways, relying on Big Data analytics “to get a real-time view of the workflow in the pharmacy, distribution centers and throughout our stores and e-commerce.”
With the help of expert insights provided by real-time data analysis, the company has been able to align inventory with customer needs and take immediate action to maximize customer satisfaction and profits. For example, they discovered that it was a very good idea to stock plenty of a certain comfort food, Strawberry Pop-Tarts, before (and after) hurricanes. Yes, Pop-Tarts can be both heated in a toaster before the power goes out and eaten cold if things go dark—could that be it? Or maybe they’re just a go-to meal for millions, regardless of coming high water or ill health. Still, the big data doesn’t lie, and this has been one easily actionable profit-generating tip for Walmart.
What else does the giant chain do in terms of analytics applications and data-driven decisions? They consult both historical data and current data to suggest item substitutes when shoppers are searching their site for similar products that could be out of stock. They use data to optimize supply-chain routes, personalize the ecommerce experience, and improve the check-out process in their brick-and-mortar stores, among other things. They have clearly mastered the art of analyzing their data.
Airlines often rely on real-time data for their various operational activities. Popular carrier Southwest is no exception: it’s utilized targeted customer data to deepen its understanding of what new services customers would appreciate, among other projects.
If you’ve flown Southwest, you know about its boarding process of lining travelers up in groups based on check-in time, paying extra, or holding frequent-flier status. It’s safe to say that the Southwest boarding process is slow, even though it may be faster than it would be with assigned seating. It’s also stressful for boarders, particularly for the last (C) boarding group, who may not even be able to secure bin space for their carry-on and will most certainly get stuffed into a middle seat.
If that’s ever been you, you’ll be glad to hear that Southwest has been working on how to get people boarded faster — as it had been able to do when it was just starting out — without resorting to preassigning seating, which, for some reason, can take up even more time that airline schedules don’t have to spare.
The company is utilizing data science and real-time data observations to determine the best ways to board planes, when there are likely to be boarding issues, and what’s behind the hold-ups. Real-time-collected data could also provide actionable insight on what could be done to improve certain types of boarding situations.
Where were we? Oh right: with the help of real-time data, data-driven organizations like Southwest and Walmart are empowered to innovate and do what works, both for enhanced customer journeys and robust bottom lines.
One area where this business strategy can be used to draw conclusions is online search and discovery. After all, there’s a ton of data involved in processing and correctly interpreting people’s search terms and then pointing them to the content they want.
Algolia, for example, is a search provider that offers near real-time analytics on billions of search queries per day. You can:
Interested in the benefits of getting more-data-driven insights for your site-search decision makers and definitively knowing your user intent? Deepen your dive into search optimization with a live demo or free start!