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As Consumer Habits Change, How Can Businesses Keep Up?

consumer

As Consumer Habits Change, How Can Businesses Keep Up?

American consumers don’t act and buy the way they did just a few short months ago – at least most of them don’t.

The pandemic and the need for social distancing led to an upsurge in online buying. Takeout and delivery replaced, at least temporarily, dining out. Many consumers, worried about the health risks of spending time in grocery stores, turned to services that would do their shopping for them.

Now, as the country tries to reopen and seek the next normal, businesses across the nation must figure out which of those consumer behaviors will become permanent, which were temporary, and whether any new ones yet unthought of might emerge.

“We live in a time when information can become outdated pretty quickly, and that’s become even more true because of COVID-19,” says Janét Aizenstros (www.janetaizenstros.com), a serial entrepreneur and the chairwoman and CEO of Ahava Digital, a company that ethically sources data on American consumers.

“The businesses that are going to succeed moving forward are those that grasp what consumers want and understand their changing habits.”

In contrast, those businesses that fail to understand what the latest consumer data is telling them, and are slow to adapt to the changes in consumer behavior, are going to be at risk, Aizenstros says.

She says going forward, businesses need to:

-Be prepared to pivot. Business leaders must be flexible. Many restaurants figured that out when the pandemic began, Aizenstros points out. Patrons could no longer dine-in, so the restaurants put an emphasis on takeout and delivery services. In the same way, each business will need to figure out how it can adapt and adjust its services or products to meet what customers want and need, she says.

-Gather reliable consumer data. With the internet, social media and numerous other sources, there is plenty of information available today about consumers, but not all of it is reliable. Make sure data comes from a quality source and that it reflects as much as possible the current thinking and behavior among consumers, Aizenstros says. “Businesses that fail to use reliable data and stay on top of the consumer trends,” she says, “will have a difficult time thriving as we go forward.”

-Take steps to make consumers feel comfortable. Even as people venture out more to dine in restaurants or shop in person, a Gallup survey shows they still plan to exercise caution. Businesses can help themselves by letting consumers know what steps they are taking to keep their stores, restaurants, and offices as safe as possible. “This is just another example of understanding and keeping up with what consumers want,” Aizenstros says.

Businesses have always had their plans and operations disrupted by both technological advancements and changing consumer habits. But rarely does consumer behavior evolve as quickly as it did in the early months of 2020 – and the changes didn’t always happen in easily predictable ways.

“Some areas such as home decor and fashion have done well recently,” Aizenstros says. “At the same time, we are seeing trends with businesses like J.C. Penney, Hertz and others struggling and filing for bankruptcy. It’s hard to keep up with consumer thinking unless your data is consistent, relevant and accurate. But if you understand what your customers want and work to give it to them, your business will have the opportunity to prosper.”

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Janét Aizenstros (www.janetaizenstros.com) is a serial entrepreneur and the chairwoman and CEO of Ahava Digital, which provides businesses and investors with ethically-sourced verified data about American consumers. Her background includes roles in finance at TD Canada Trust, Canon, and Brookfield LePage Johnson Controls, along with management consulting in a broad range of functions, such as supply chain operations, data analysis, and strategic thinking. She has a doctorate in metaphysical sciences with a specialization in conscious business ethics.

data science

10 Data Science Projects E-Commerce Businesses Are Using

Today e-commerce businesses are using data science in many different areas to stay ahead of the competition. For instance, e-commerce sites are investing funds into personalizing shopbots to enhance customer experience and recommending products to buyers based on browsing habits and previous purchases.

Selling the best products only works if e-commerce businesses can identify who wants to buy them and recommend them when these customers are ready to make a purchase. Here are some ways e-commerce businesses are utilizing data science to enhance the customer experience.

1. Retain customers

One concern for every e-commerce business is customers switching to other e-commerce websites. Customer retention is crucial if a business is to expand and grow. There are many benefits from having loyal customers, such as receiving real-time feedback from them and having them recommend products or services to others.

A churn model provides metrics such as the number and percentage of customers lost to the business as well as the value and percentage of this loss. When a company is able to identify customers who are most likely to switch to a different e-commerce site, it can take actions to try and keep them.

2. Give product recommendations

Using big data analytics offers a way to understand the shopping behavior of customers and predict patterns. For example, being able to establish which brands or products are most popular when spikes in demand for certain products occur or times of the year when customers shop more can help to determine the right strategies.

Recommendation filters for a particular user are based on past searches, purchase data, reviews read, etc. and allow a personalized view. This helps users with the selection of relevant products.

For example, if you’re looking for a mobile phone on an e-commerce site, there is a possibility that you might want to buy a phone cover too. Deciding whether this is a possibility might be based on analyzing previous purchases or data searches of customers.

3. Analyze customer sentiment

Gathering customer feedback is very important for e-commerce sites. Using social media analytics, data science and machine learning, companies can perform brand-customer sentiment analysis. Natural language processing, text analysis, data from online reviews and online surveys are just some ways to analyze customer sentiment.

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4. Predict the lifetime value of customers

E-commerce businesses can benefit from knowing what net profit a customer is likely to bring to the company. Being able to predict the lifetime value of a customer can help with factors such as defining objectives for expenditure, optimizing marketing strategies and deciding cross sell and up sell according to customer purchases.

By using data science models to collect and classify data, e-commerce businesses can predict future buying behavior and have more understanding when formulating business strategies. They know which customers are most loyal and can decide where spending money on advertising etc. will offer the most return on investment.

5. Manage Inventory

Proper management of inventory is essential for e-commerce businesses. When customers are unable to get what they want when they want it, it’s a major deterrent to retaining them. They will simply move on to the next company that can offer this. They want to receive the right goods at the right time and in perfect condition.

The maintenance of the supply chain has become complex today and using inventory data analytics enables businesses to manage inventory effectively. Using machine learning algorithms and predictive analytics enables patterns to be detected that can define inventory strategies.

6. Detect fraud

Living in a digital world where millions of transactions are taking place consistently makes fraud detection essential. Many different forms of fraud are possible and fraudsters are becoming smarter every day.

E-commerce businesses can detect suspicious behavior by using data science techniques. Signs of suspicious behavior could include a shipping address differing from a billing address, an unexpected international order or multiple orders of the same item.

Common data science techniques to detect such behavior include:

-Matching algorithms to estimate risks and avoid false alarms.

-Data mining to address missing or incorrect data and correct errors.

-Clustering and classification to help detect associated data groups and find anomalies.

A fraud detection system helps companies to decrease unidentified transactions and increase company revenue and brand value.

7. Improve Customer Service

A customer is central to any business, especially e-commerce. Personalizing services and giving customers what they really want and need is essential to keeping them happy. Big data analytics offers businesses the potential to enhance their processes so that customers enjoy transacting online.

Natural language processing allows customers to communicate with voice-based bots and data can be stored for future purposes. When businesses know more about their customers and what they want, they are able to devise the best strategies to improve their customer service.

8. Optimize prices

Data-optimized pricing is making some retailers plenty of money. Many online retailers, such as Amazon, Home Depot, Discover and Staples, vary their pricing based on secret formulas. Cost analysis, competitor analysis, and market segmentation are all critical when it comes to pricing.

Pricing of products can impact a business in many ways when it comes to market share, revenues and profits. A key for retailers is to be able to figure out the right price and with big data analytics, they are not only able to determine that number for the market in general but also calculate it with some precision for individual customers.

9. Make online payments easy

Many e-commerce sales are made via mobile platforms and online payments must be secure and safe for customers. Big data analytics helps to identify anything that threatens the process and helps to make online shopping safer.

Various payment options make the online payment process easy and convenient for customers.

10. Determine the quality and reliability of products

E-commerce stores usually provide warranties for products that allow customers to deal with any problems at no cost during the warranty period. Analytics relating to warranty claims can help to determine the quality and reliability of products.

If manufacturers are able to identify early warnings of possible problems, they may be able to address them in time to avoid serious damage to the business.

Text mining and data mining are two techniques that can be used to identify patterns relating to claims and problems with products. The data can be converted into real-time insights and recommendations.

The bottom line

We’ve taken a look at the ten ways that data science models can impact e-commerce. There are so many e-commerce websites and many of them sell similar types of products. Data science helps e-commerce businesses to understand and analyze customer behavior and provide ways to enhance customer service.

When companies understand what they do best and who their loyal customers are by using data science, they are able to improve product designs and customer service, formulate better pricing strategies, manage inventory effectively and provide secure online purchasing and payment options.

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This guest post is contributed by Kurt Walker who is a blogger and college paper writer. In the course of his studies he developed an interest in innovative technology and likes to keep business owners informed about the latest technology to use to transform their operations. He writes for companies such as Edu BirdieXpertWriters and uk.bestessays.com on various academic and business topics.

container

How to Take the Risk Out of International Container Logistics

Who is reliable enough to trust with my assets? This was the main question of people after my keynote about “How to take the risk out of container logistics” at Intermodal Europe in Hamburg. Trust is basically the most important ingredient when doing business with a partner: Will they return my containers on time? Do I have to follow up on my invoices? Can I easily reach my partner when I have questions? Without a certain level of trust, you would probably not make deals with a company, even though the offered price seems cheap.

Over the last decade we have built long-lasting relationships with partners where trust was not an issue, but now two things have changed: (1) Digital technologies allow us to collaborate with basically the entire world in no time and (2) stakeholders are increasingly asking for transparency e.g. to better understand where the products they purchase come from. To adapt to these changes, we have to redefine “trust” and find answers for how to make time-efficient and risk-free deals with partners you have never worked with before. 

Trust is an everyday problem in logistics 

The lack of trust is an everyday problem for most container owners and users with a high impact on the decisions they make. Let me give you a few examples: Imagine a container lessee returns your equipment too late or in bad condition. Of course, you might receive per diem fees to compensate you and the DPP (Damage protection plan) covers damages but how do you explain that to your next customer who is waiting for these boxes? How much time does it cost you to follow up, arrange container inspections and send emails back and forth?

Imagine if you bought a used car and the condition was completely different from what the seller had told you before, you would probably not work with the same seller again in the future (and I bet you would also advise your friends against buying his/ her cars). What happens is that operational costs increase due to the lack of trust, Maersk, for example, announced random container inspections because of misdeclaration of cargo. Increasing costs and high risk ultimately leads to something everyone probably has already said at least once: We only work with people we know.

What is currently being done to mitigate risk?

Most of the time decisions are made based on gut feeling or anecdotal evidence from your network, the press, Google or sometimes just a random Linkedin post about a specific company. In addition to that, personal meetings and extensive travel are still the standards for vetting a potential new partner before setting up bank guarantees, credits assessments and “triple-checked” watertight contracts by expensive layers. It’s not only incredibly difficult, time-consuming and expensive to collaborate with new partners but also not real-time, non-scalable and error-prone. Such partner vetting processes lead to fewer partnerships, less market transparency and slower speed- which makes no sense in times of real-time communication, cost pressure and the increasing need for market transparency.

In today’s digital age, there must be a better way. Why? Because you won’t have the time to initiate your traditional vetting process when a potential customer is reaching out. If you want to get new deals, you have to be the first one with a quotation.

Other industries rely on platforms as neutral data layers

To create trust, we can learn from how other industries have increased trust through platforms as neutral data layers, data standards as the common language, user-generated content and financial credit scoring models. May it be Amazon or Alibaba for buying and selling products online, Trustedshops for e-commerce or Delivery Hero for ordering food online – Other industries rely on platforms as neutral data layers. Take Alibaba as an example: Would you buy from a small, random company that you have never heard of just because the price is cheap? Most likely you would not. On Alibaba you do so because you trust their platform, the Alibaba insurance and their vetting process.

Moreover, you trust your peers and you look at how other partners have rated that company in past transactions on Alibaba. That’s why most online platforms have introduced performance reviews & ratings. You would probably rather buy from a seller on Amazon with thousands of 5-star reviews instead of someone with barely any ratings.

With Container xChange, we can see the same happening in container logistics. Since we introduced peer-to-peer reviews and ratings we have seen an increase in transactions by 17% for top-rated companies (>4 stars on average) and overall it has led to faster replies, release documents and a greater level of trust because members now have a bigger incentive to be a reliable partner. Another great example of how platforms in other industries leverage technology are payment and loan providers such as Klarna or even retailers like Ikea with next level credit scoring models. Instead of gut feeling, they can now, for example, even include signals from social media with their algorithms to forecast creditworthiness – which speeds up vetting processes and decreases human-made errors significantly.

May it be reviews, credit checks or vetting – I think we can do the same in logistics. Platforms like Freightos (for freight forwarders and shippers), Xeneta (freight rates) or Container xChange (asset-sharing in container logistics) already exist, but in the end, it comes down to your behaviour. Make credit checks for your partners as easy as possible, be reliable and stick to what you agreed on. Becoming a trustworthy partner yourself is the first step to a greater level of trust in logistics.