Supercharging 2019 Brand E-commerce Performance
Navigating the Treasure Trove of Data to Drive Business Success
Reading Time – 10 Minutes
MORE MARKETS SHIFTING TO ECOMM
But many brands will fail without deeper, actionable insights
The challenge: capturing & understanding e-commerce performance
As companies gear-up for commerce in the new year, it’s a fine time for new thinking.
There is a massive tectonic shift taking place, from brick and mortar selling, to online buying and selling that is not unlike an actual earthquake.
“Too many brands fail to accurately understand what the digital-conversation tremors mean in mission-critical business terms.”
In the “real” world, a seismic stiff-arm shakes and displaces the ground, knocking down buildings and highway overpasses, triggering landslides and even volcanic eruptions.
In the online world, the transition of consumers to e-commerce knocks down not only existing brick-and-mortar companies, but even online only businesses, triggering decline because those brands fail to accurately understand what the digital-conversation tremors truly mean in mission-critical business terms.
We’re talking about what companies may be missing in the ‘Big Data’ fire-hose of ‘hidden’ market information on:
- buyer preferences
- customer experiences
- sales conversions product failure rates
- overall brand performance
- future sales potential
- up-and-coming disrupters
Meanwhile, if you’re a Brand Insight Professional, without a pulse on your ecommerce performance, you will be wringing your hands and losing sleep knowing that you:
- don’t have the time to gather the right customer sentiments online
- can’t make sense of the complexity of the data
- can’t filter out the tsunami of “noise,” to hear what’s actionable
- are battling leadership who consider digital research to be too costly a resource drain
WHY YOUR WORLD’S EVEN SHAKIER
E-comm data is growing as customers use it for research, leading to the ultimate buying decision
Forrester says that this year U.S. shoppers will spend some $500 billion online. While it may not be earth-shaking news that ecommerce is thriving, the double nature of online behavior means brands can’t just pay attention to what customers do and say in and after a purchase decision. You must also be hyper aware of what people see, ask and hear from others in their “research phase,” often days and weeks before they take out their credit cards.
For instance, Amazon, No. 1 among the Top 500 internet retailers, says that the vast majority of U.S. adults make an online purchase from them every year. Why does this matter? Because now, more than half of U.S. adults use Amazon as a “research resource.” This online research is now a critical component for shoppers both online and at brick and mortar stores.
If your brand is not listening hard to customer reviews, you must start.
To get your head around the power of reviews, note that the magazine Inc. reported:
READ ONLINE REVIEWS
91 percent of people “regularly or occasionally” read online reviews
84 percent trust online reviews as much as a personal recommendation
64 percent form a (brand) opinion after reading 1 to 6 online reviews
This means that virtually all of your customers are using the web to educate themselves and make purchase decisions, and forming loyalties based on what people are saying on sites featuring reviews.
Companies of all sizes are striving to figure out the best way to keep a pulse on their customers. With oceans of customer comments and reviews available at their fingertips, this can prove to be a daunting and resource intensive task. Selecting the right vendor to partner with is crucial in bringing meaningful and actionable brand insights forward.
However, when you get down in the weeds of the data you get back, you must also have confidence in how the results were decided upon:
- what are the terms, words and phrases your chosen flavor of A.I. is listening for?
- how did the bots’ algorithms interpret what they “heard” in what they are telling you?
- can you trust a machine to deliver accurate views of products and services?
Clearly, you must be careful to partner with a Big Data vendor employing an algorithmic platform that can distinguish and filter finely in the face of the countless nuances of context in opinions. Online comments are typed in a myriad of “natural language” and brand-and category-specific words, phrases, even slang that real people use. Without this level of depth in the AI and platform used, the deep-learning machines are like globetrotting tourists who can’t understand what people say when they get off the airplane.
“How did the bot’s algorithms interpret what they heard in what they tell you?”
Equally as worrisome as the sheer bulk and complex nuance in online conversations, reviews and comments are what you are given by your brand-insights partner as research results. Ask yourself how much an algorithm is responsible for your expensive insights? Because raw data still has to be meticulously interpreted by experts. Yes, in a traditional consumer-insights exercise, your human vendors and in-house people are good interpreters. They’ve been working with focus groups and a host of other traditional techniques for decades. They’re good at deciphering from the data what pains the customer, and what products and services would comfortably satisfy their dreams. But when dealing with bots that have extracted words and phrases from billions of pages on the web, wouldn’t it be nice if the machines had a ghost of an idea about what makes the data useful and how it can be leveraged? Of course. But it’s rare to find a Big Data source that has “brand expert” A.I.s.
For that to occur, you need a software platform created by cybernetic engineers working in close quarters with living, seasoned brand insights and marketing people who “speak the native tongue.” (Like the bilingual tourists alluded to above). The good news is, there are such vendor choices in the forest of Big Data trees out there. You should spend some time finding them. If you do, there are other questions you should be asking them. Read on.
QUESTIONS YOU SHOULD BE ASKING
This first question is kind of a software engineer-meets-brand-expert query. Naturally, they have software engineers. But do they have branding people who confer with the computer experts, closely, in the same organization? In a nutshell, you’re asking your potential Big Data partner what’s the basis for the consumer insights the bots profess to deliver?
1. What’s your scoring methodology?
This first question is kind of a software-engineer-meets-brand-expert query. (Since they certainly employ the work of software engineers, if not branding people) and you are working in brand expertise. In a nutshell, you’re asking your potential Big Data partner what’s the basis for the consumer insights the bots profess to deliver?
- What types of specific words and phrases are your algorithms listening for?
- How can I be sure the machine has learned enough about the kind of information I need?
- How do you gauge the efficacy of our products and services against competitors?
- How does the software consider various categories of products?
- How do you determine and score such things as “loyalty” and “referrals”?
- How can your algorithm’s results give us a picture of our overall brand health online?
2. How easy is it?
You’re busy enough in your daily routine at work, aren’t you? Why invite a migraine? Third-party-insights data can produce a dilly of a headache. And if it’s not easy to see actionable data, why use it in the first place? Questions you should ask include how easy is it to get visual-at-a-glance snapshots on a regular, up-to-date basis for critically actionable learnings such as:
- Where does my company stand currently online in our various categories?
- Can I see product strength across multiple brands?
- Do you filter results by individual products?
- Can I easily see the worst-reviewed products?
- Can I aggregate all your sources, and even go down to store level in the data?
- What’s the current situation in e-commerce for our loyalty and referrals?
- Can your software make equity comparisons?
- Can you validate our current market effectiveness?
- Can I easily see and articulate to others our overall e-commerce performance?
- How easy is it to export and place data in my documents and Power Points, et al?
- How do you gauge the efficacy of our products and services against competitors?
4. How predictive can you get?
It’s gratifying in brand marketing to influence human behavior. It’s is absolutely sublime to have some success in predicting human behavior at bring-out-the-wallet time. This is often thought of as the Holy Grail of business, of course. Everybody wants a crystal ball. Why wouldn’t they? Amazingly, one of the benefits of the artificial intelligence explosion is we are starting to see some indications that computers (again, backed up by seasoned human beings) can deliver some impressive predictions which time validates. So, a simple question to ask is:
- Do you have any confidence, or experience with clients in the ability of your data to map to actual sales down the road?
Bonus: What else can you show me?
Finally, (besides gauging the PRICE of the service) you might ask your Big Data insights vendor how they can sweeten the relationship with added value. Such questions include:
- What other kinds of things can your service give to make my life easier and my business more successful?
- What up-to-the minute alerts can you provide?
- Can I have phone conversations with your brand analyst experts?
I know that that sounds like a lot of information to consider when looking for a Big Data insights vendor. But if you really want to supercharge your online commerce results you’ve got to be informed and rigorous. If you are, be prepared to love how much better your web-based commerce gets and how much happier the people you work for will be. That would help make 2019 memorable wouldn’t it?