eCommerce has experienced staggering growth over the past few years. In 2020, consumers spent $791.7 billion online — a 32.4% increase from the previous year.
This trend shows no signs of slowing down due to several factors. However, staying competitive in eCommerce isn’t getting any easier. The online retail landscape is quickly evolving, and brands must keep up.
In this article, we’ll cover several different eCommerce analytics and omnichannel strategies brands need to adopt — and which ones to drop.
Keep reading to learn more.
The Changing Landscape Of eCommerce
There has never been as much opportunity in the eCommerce industry as there is today. With factors like the pandemic making people increasingly reliant on online purchases, customers are becoming more selective about who they buy from.
Brands are beginning to see plummeting returns on ad spending. As a result, they are prioritizing customer lifetime value and pushing for better brand loyalty.
Trust is a critical currency right now and in the future for eCommerce brands. When it comes to staying competitive, brands need to be authentic, transparent, and always hyper-aware of their customers on every channel that customers are active on.
Most successful companies are now beginning to rely on new strategies that allow them to act much faster while gaining more in-depth data on customer-centric selling with an omnichannel approach.
Using cutting-edge technology, they can understand their customers on the deepest level while getting the information quickly.
Data Sources Brands Are Relying On
Thriving in this new eCommerce landscape needs new levels of unique customer insight — and quickly. Many strategies that brands have relied on before will no longer be enough to keep up with the competition.
Let’s take a look at several different data sources that brands in the eCommerce industry rely on to gain insight into their customers and their pros and cons.
Because of how overwhelming it can be to have so many options of data sources, it’s essential to know what to prioritize for your brand (and to deprioratize).
Agile research is an approach to research that allows brands to get data quickly, early, and often. Eliminating dependencies and friction that can slow down traditional research strategies, it trades precision for helpful feedback so that companies can learn quickly.
Agile research has many benefits. It is much more flexible and essential for brands with frequently changing customer requirements or priorities. When changes need to be made, project teams can quickly make changes.
Agile research also allows products to market faster because they can test different iterations of their product more quickly.
However, the tradeoff creates quite a few disadvantages as well. In an agile system, there is much less predictability. Because agile research depends on continuous improvement and customer feedback, it can be hard to predict profits. The major focus is to get products to customers quickly, which can lead to other issues.
Another issue that agile research brings is that it can be challenging to have all team members on the same page for a singular goal. Transferring information from one department to another can lead to internal inefficiencies.
Consumer Data Tech
Customer data tech (also known as customer analytics) examines customer information and behavior so that a brand can identify, attract, and retain more customers.
Today, companies have a vast pool of data to draw from to get more information about their customers. This is important for brands to personalize their marketing and sales strategies to attract their audience and stand out among competition.
The goal is to deeply understand customers’ buying habits and lifestyle preferences so that they can predict their behaviors and optimize their customer journey.
However, there are cons to these data collection methods. Because of how much data is being produced by customers every day, it can be very costly for brands to store and retrieve this data. Companies will need tech specialists to operate these data centers.
Another con is the risk of security breaches of sensitive data. The threat of cyberattacks can bring liabilities to organziations (plus the additional costs of better online security).
Lastly, the analysis of this data takes time and workforce. In today’s hyper-competitive eCommerce landscape, it takes a robust and highly skilled team to maintain these datasets.
Marketing automation has evolved from simple email marketing to entire platforms streamlining different marketing processes.
Marketing automation can be great for:
- Finding high-quality leads when they are ready to buy
- Lowering cart abandonment
- Saving time for marketers
- Reducing staffing costs
- Scaling processes
Although automation tools do make life easier for marketers, there are limitations.
Successfully automating campaigns takes comprehensive planning from top to bottom, which can take a long time. If there are any inefficiencies in your processes, it can be challenging to iron them out.
Another eCommerce strategy that CPG brands use to get customer data is the use of focus groups. Focus groups usually consist of 10 or fewer volunteers discussing an idea or product of a brand.
A research team will ask questions to get participants to share their ideas, opinions, and reactions. This has been a standard tool used by the advertising industry to measure a product’s impact.
Focus groups are great for measuring customer reactions easily. They can quickly find areas that need improvement and iron out product requirements by the end user. It’s also helpful in giving insight into your company’s positioning vs. competitors.
Because of the smaller size of focus groups, it’s a relatively quick process compared to other research methods.
However, focus groups do not provide in-depth data compared to other forms of market research. Due to their small sample size, focus groups don’t guarantee an accurate representation of the market as a whole, which is a key reason that organizations have begun to move away from this tactic.
Another downside is that focus groups are much more expensive than research methods like surveys and questionnaires. Although some people will volunteer their time for free, most must be compensated.
Another standard method to gather customer data is through the use of surveys. Surveys are great for collecting qualitative feedback. With open-ended questions, brands can get deep insights into how their customers think about their products or services.
Surveys are also more direct than trying to interpret behavior data. Although poring over customer data and watching their online behavior can be helpful, many resources must be expended to get the necessary tools to analyze the data. On the other hand, surveys are much more direct about what questions need to be answered.
Another benefit of surveys is that they are much cheaper than other research options.
However, surveys provide sampled data — not complete data. Only a tiny proportion of people invited into a survey will respond, which means you might not even get data from everyone you want it from.
Responses to surveys are inherently subjective as well. It can be hard to get entirely rational responses to your questionnaire.
Lastly, getting the results from a questionnaire takes valuable time. When adapting to the market, time is of the essence when getting customer feedback.
The Future of Omnichannel Data Collection and eCommerce Analytics
Gathering valid customer data in a short amount of time and drawing significant customer insights from them is no easy task. However, there is a better way to do it.
Forward-looking companies are beginning to use customer feedback and advanced AI technology to increase the time to insight and understand customers. There’s a vast, valuable source of data that just about every brand has access to — reviews and ratings.
Companies are beginning to rely on sentiment analysis using product review data, which helps them find valuable insights directly from their customer feedback.
The simple fact is that people love to talk about the products that they buy and use.
Through modern AI tools like Natural Language Processing (NLP), computers can decipher human language within seconds. It can comb through data in forums, blogs, reviews, social media posts, and more.
Customer sentiment analysis tools are one of the best things a brand can add to its arsenal to increase conversion rates quickly. They can help companies understand exactly how their customers feel about their products in real-time. Then they can quickly apply these insights to make fast changes to their products and messaging.
Benefits of review analysis tools include:
- Improving customer experience
- Optimizing marketing claims and PDP’s
- Building stronger customer relationships
- Finding competitor weaknesses
- Saving time and money on market research
- Identifying white space in the market
Review analysis tools are the future of customer data collection.
Get Deeper Insights From Your Customers
That’s some of the major players of omnichannel eCommerce analytics. The online shopping landscape is quickly changing, and CPG brands must adapt.
Don’t settle for outdated data collection methods. Get a deeper view of your reviews, ratings, and actionable insights to boost your conversion rates.
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Yogi’s K-means-based AI platform analyses Reviews & Ratings at the deepest level possible across all competitors and retailers. Save time searching for insights so you can move the needle for your brand today.