These Married Co-Founders Poured Their Life Savings Into Their Company. Then a Mistake Almost Cost Them Everything

In 2017 Farzan Dehmoubed, a marketer, and his wife Jennifer, a schoolteacher, created the Lotus Trolley Bag, a set of washable bags with attached rods that can be hung inside a shopping cart. The bags, with features like secure pockets for egg cartons and wine bottles and an insulated pocket for frozen foods, quickly became the top-selling reusable bag on Amazon, and are now sold in stores like Wegman’s, Albertson’s, Kroger, and TJ Maxx. But getting to that point required overcoming a mishap that nearly sunk their startup. –As told to Kevin J. Ryan

We invested $45,000 into our first inventory. It sold out in 10 days. We were really excited. We called up our manufacturer and placed another order. We wired them $50,000–everything we made on the first batch and more.

Six weeks later a big container arrived. We had our friends and family help us unload it. We opened up the boxes and looked at the product, and it was nothing like the first set of bags. It looked the same from a distance, but when you actually looked at the stitching and the quality of the printing and the logo, it was not what we had ordered. My wife and I looked at each other and said, “This can’t be real.”

I remember thinking to myself, ‘We can fix this, maybe it’s just some loose thread.’ But it wasn’t salvageable. We placed a complaint with the manufacturer, even though we knew it wouldn’t go anywhere, since we were just a family business with very little leverage. We later learned it had outsourced the order to save pennies on the dollar.

We decided pretty quickly we couldn’t sell the bags. We didn’t feel comfortable putting our name on them. That meant we would have to take the $50,000 loss. I don’t think Jenn and I talked for the rest of the day. It took a day or two to absorb the shock. 

Even though the manufacturer promised us they would do better the next time around, we weren’t going to be fooled twice. I flew to multiple manufacturers in Vietnam until we found a new one we were happy with. We hired a third-party quality check company. When the goods were ready to ship, they would go in and do an audit: open up each box and check them, and send us videos. We kicked ourselves for not doing that in the first place.

We placed a new $50,000 order, which required emptying our life savings and practically maxing out our credit cards. It was two months before the new inventory came. We were pretty upfront with our customers during that time. We told them very frankly: The bags didn’t come out the way we ordered them, the shipment is going to be delayed, and we really thank you for your patience.

I think letting your customers know you’re just like them, and that you’re just trying to provide a product that they’ll be happy with, goes a long way. People related to us. They were very understanding.

We still had a lot of orders canceled though, and we gave discounts to customers who had been patient. We were nervous when the new container came–if the product was bad, we would have lost everything. But it was exactly what we’d ordered. We sold out almost right away. Because of the discounts, we didn’t make much money at all on that order, but we had our reputation.

Not putting that product on the market was one of the best decisions we ever made. If we had, I can guarantee you we wouldn’t be where we are right now. It would have killed our reviews. It would have ruined our brand.

We now have a 4.6-star rating on Amazon with more than 700 five-star reviews. We’re on pace for $3 million in sales this year. We just launched our second product, a reusable produce bag, and those same early consumers are buying it.

As a business owner, you have to make your decisions for the long-term. For us to take that financial hit was scary, but we had bigger goals in mind. We got through it. And we made a lot of loyal fans in the process.

By Kevin J. RyanStaff writer, Inc.

Source: These Married Co-Founders Poured Their Life Savings Into Their Company. Then a Mistake Almost Cost Them Everything

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Every business has risk associated with it. In this video Mr. Ashok Ajmera in very simple words talks about various kind of risks and how to manage them which can be very useful in any business.


Meet The Billionaire Who Defied Amazon And Built Wish, The World’s Most-Downloaded E-Commerce App

On a sun-filled San Francisco afternoon, Peter Szulczewski is climbing the stairs to the top of a Sansome Street skyscraper, past floors filled with Wish data scientists and engineers, pool tables and DJ equipment. Large windows give way to a stunning view of the city. But most of Szulczewski’s customers don’t work in offices like this or live in Northern California coastal enclaves. In fact, most of them don’t have much money at all. Wish’s customers are typically working-class Americans from places like the Florida Panhandle or East Texas, Dollar Store shoppers who find Amazon Prime’s $120 annual membership too rich for their blood……..

Source: Meet The Billionaire Who Defied Amazon And Built Wish, The World’s Most-Downloaded E-Commerce App

Chatbot Development – Don’t Run in the Conversational UX Race – Sam Makad


Businesses today want to boast two medallions – artificial intelligence and machine-learning. As a result, they try putting AI into every potentially-automated process. But is this necessary?

While AI is the answer to several business issues; it is still evolutionary. It has a lot of room for improvement. Can AI be trusted with customer interactions? Yes and no.

If you have been thinking about implementing an AI-based conversational chatbot on an automated interactive user experience, you may need to think again. Consider the following arguments:

1.   Do you want to eliminate all human interaction from automated business processes?

Conversational chatbots do not have an unlimited decision-making capacity; in fact, they are very focused. A conversational chatbot can only make conversation based on its training, but using a trained conversational chatbot still results in impediments when interacting with humans. When a chatbot that was designed to provide incremental interaction efficiency with customers cannot fully deal with them, it becomes a liability you don’t want.

One negative bot experience can result in serious business damage.  Of customers whose initial bot experience was negative, 73% of them won’t interact with it again. Chatbots run at 85% efficiency – less than the 90% customer satisfaction rates provided globally by call centre executives.

Chatbots should automate redundant processes. How? Every company has a set of queries frequently asked by customers. Using these rules, chatbots help customers; however, a human is still needed to solve the communication puzzle. This is imperative because any query can result in an anomaly that the chatbot hasn’t been trained for.

2.   Does Your chatbot have sufficient decision-making capabilities?

Many people believe the most complex part of running a business is the sales or finance aspect; but it’s the decision-making. Humans are intuitive and pragmatic enough to make the occasional sound decision. Even when facing a problem for the first time, a human can make sense out of and deal with it. Therefore, it is logical to provide human employees with a certain degree of freedom when interacting with customers.

For instance, allow your customer representatives to give freebies or special offers when dealing with customers who’ve experienced issues while working with your business. You can do this because you trust their decision-making skills. Can you say the same for a chatbot? Many times, customer executives act outside their job description to leverage their business relationship when helping a customer. This does not immediately affect the business’ bottom line but it does help establish a relationship with the customer. Can a chatbot do the same?

Some say the very essence of neural networks in machine-learning (ML) is to break processes and learn the art of decision-making. That said, any machine-learning engineer would state that ML continues to grow and has miles to go before it will reach the general AI stage. Can you trust this kind of evolutionary technology with decision-making? A chatbot is unable to go beyond its programming to assist customers.  A chatbot cannot be trained on complex scenarios where it would need to think “outside the box” to do something. Such actions are subjective, so it is counterintuitive to expect them from your conversational chatbot. It cannot handle complex conversations.

3.   Does the idea of posing your chatbot as a human seem fascinating to you?

For many entrepreneurs, incorporating AI into their business processes is something they want, rather than need, to do. This is because they want to see themselves as pioneers in the field. They want conversational chatbots in their processes that are as seamless as human customer executives; but here’s a staggering fact for you – 75% of people want to know whether they are talking to a chatbot or a real person.

The idea of a chatbot being as conversationally fluent as a human may work in science fiction, but real human customers have different expectations. So, if your desire for a conversational chatbot is based on your belief that it will amaze your customers, you should reconsider your decision.

4.   Do you have the data, process redundancies and infrastructure to deploy a conversational chatbot?

Human interactions bring empathy to a conversation. This applies to both the executives, who can interact with greater empathy and the customers, who will be more considerate towards an executive. Unfortunately, empathy disappears when chatbots completely dominate the conversation. In fact, 61% of people find it more frustrating when a chatbot, rather than a human, is unable to solve their problem.

To make the conversational chatbot more effective, you need to have the right start. First, determine whether the process has enough redundancy for automation. Second, confirm the appropriate data for chatbot training purposes, because the data used in building is not sufficient preparation for actual human interactions. This is important because customers are less tolerant when a chatbot fails to resolve their queries.

Finally, review the infrastructure to ensure the chatbot has the technological space it needs, i.e.  can it integrate with the ERP and CRM systems while still providing secure processes? Businesses need to answer these questions before choosing to deploy a conversational chatbot, and they need to consider their intent behind deploying one.

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