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Beyond the Hype: How to Pick a Technology Partner for the Long Haul

Learn how to pick a technology partner for expanding on Amazon and the difference between algorithmic and rule-based repricers. By Stan Spring September 21, 2018
Beyond the Hype: How to Pick a Technology Partner for the Long Haul
Stan Spring
About the Author

Stan Spring is a freelance tech writer for Feedvisor. True to his Louisiana upbringing, he likes to cook, play guitar, and shoot the breeze on a sunny day.

Your competition is increasing, your catalog is expanding, and your growth is leveling out. On the one hand, pricing your products manually is no longer very efficient or effective, and on the other, you need a repricer, and technology partner, that can lead you to both short and long-term success.

Sooner or later, every large Amazon seller and retailer will come to this crossroads. It’s a big decision that can impact how fast your business scales — with hundreds of thousands of dollars in future profits at stake. Not to mention, your competition is likely exploring their own options.

Which repricer do you choose?  

Buzzwords Might Cost You a Premium

Before making any choices at all, you need to understand the difference between the two major types of repricers: rule-based and algorithmic. In this post, we cut through the industry buzzwords to reveal the key differences. We also give you five tips to help you read between the lines when evaluating a technology partner.  

Amazon repricing is a young, niche industry driven largely by software innovations. It is no surprise then that many different companies like to throw around buzzwords like “artificial intelligence”, “machine-learning”, “big data,” and “algorithmic.” Unfortunately, these words tend to very selectively apply to various repricers on the market or just don’t apply at all. In essence, what many customers end up actually buying are overpriced rule-based repricers.

Not All Algorithms Are Sophisticated

Many repricers that claim to be “AI-driven” or “algorithmic” are actually rule-based repricers in disguise. Here is one tell-tale sign of a rule-based repricer: Some companies will tout that they have an algorithmic repricer or use AI in some way; however, in those same marketing materials, they will also suggest using “custom rules”, “algorithmic rules” or “rule-based settings” to give you more control. Both of these claims taken together suggest that these companies lack confidence in their algorithms or AI. In theory, more control is great and necessary to have; in practice, an algorithmic repricer should free sellers and retailers from the burdens of customizing rules for each SKU.

As you will learn later in this post, a sophisticated algorithmic repricer that actually does utilize machine-learning requires significant staffing, research, and technological resources.

Rule-Based Repricing

Rule-based repricers operate based on pricing rules that are preset by the software or set by the seller. Each rule tends to consist of a rote business logic informed by a limited range of data on the seller’s store and competitors’ prices. For example, rules can be set to match the lowest price on the market, beat the lowest price by a certain dollar amount, or be in the lowest 20% of all prices. If one rule does not apply, the repricer will cycle through its logic of rules until it gets to one that does apply.

Ultimately, your success as a seller depends on your approval of which rules to apply. To account for market dynamics and remain competitive, you may have to change those rules on a SKU-by-SKU basis. You can also choose rules in bulk for multiple SKUs; however, this approach only underscores that your selling isn’t algorithmically optimized over time.

The rules that you end up choosing for products can also become conflicting. They need to be constantly managed to ensure that they deliver the best possible performance in a variety of marketplace situations. That burden — one you were hoping to escape by buying a repricer — can be far too great for any Amazon seller, especially with an extensive catalog of products to manage. You will also need the time or staff to improve other aspects of your business, like sourcing new products and cultivating positive seller ratings.

Aside from the issues mentioned, the other overwhelming issue with this approach is that it allows for human bias and error. Even if a seller and competitive Amazon data are informing a rule-based repricer, it is still guided by a human. In an increasingly data-fueled marketplace, sellers need an AI-driven solution that can rapidly make sense of a wide range of real-time data and apply winning strategies that do not end in price wars.

When it comes to rule-based repricing, the results speak for themselves. At best, a seller may win the Buy Box and convert some sales but not nearly as much as they could with a sophisticated algorithmic repricer. At worst, a seller could choose to apply the wrong rules entirely for their catalog or their top SKUs and risk derailing their entire Amazon business.

Sophisticated Algorithmic Repricing

In actuality, a sophisticated algorithmic repricer does not require you to construct the rules or logic for selling over and over again and does not require you to adjust them over time. You provide the cost of each product, then the maximum and minimum prices at which to sell them.

The algorithm can then find your competitive advantage in the current selling arena by leveraging vast quantities of data and by monitoring everything from the competition to price fluctuations to Amazon’s seller rankings, among many other variables. Even if longstanding sellers leave the arena and new ones enter it, the algorithm can still optimize your performance whereas rules have trouble adapting.

In Feedvisor’s case, our algorithm also takes into account the impact of data on other sellers, even if that data is only known to Amazon. For example, let’s say there are twenty very highly rated sellers all offering one type of refrigerator. One day, a seller gets a complaint from a customer saying that they are selling fake products. That seller’s performance will degrade over time because of the complaint but only Amazon has this data. The algorithm can detect the change in performance and adapt as needed, while a rule-based repricer cannot.

In addition to this advantage, a sophisticated algorithm can find the sweet spot between maximizing profits and increasing Buy Box share. In contrast, rule-based repricers boost sales and gain Buy Box share frequently by lowering prices. In turn, sellers miss out on opportunities to sell their goods at a higher price and maximize profits.

A sophisticated algorithmic repricer can achieve stronger results because it can account for the many different data points across a seller or retailer’s store, the competition, and the variables that Amazon tracks. Through machine-learning, it can understand and dynamically respond to the market. For granular tracking and building a smarter overall business strategy, it also offers better reporting and deeper data-driven insights.

What It Takes to Support a Sophisticated Algorithmic Repricer

A sophisticated algorithmic solution is a premium-priced solution because it requires an array of cutting-edge resources. Those include the leading data-driven technologies, software developers, years of research and data-modeling, experienced customer support with proven Amazon expertise, and a team of data scientists and engineers. All of these resources are critical to properly maintaining, developing, and scaling a robust machine-learning solution.

Selecting a Technology Partner for Long-Term Growth

Selecting a repricer is really all about choosing the best technology partner for your Amazon business. You could try to go from one repricer to another to see which one performs best; however, you will spend valuable time and money onboarding your business and then evaluating each solution. Scheduling a live demonstration is a better, more conservative approach that allows you to see the user interface and its workings firsthand, but is by no means a guarantee.

If stable, long-term growth is your goal, then you should probably try to choose the best technology partner from the very beginning. Ultimately, you want one that can offer you new solutions to help meet tomorrow’s challenges as you scale and as Amazon evolves.

Tactics for Looking Beyond the Buzzwords

There a number of tactics that we recommend which can help you cut through the buzzwords and marketing hype when selecting a technology partner.

Industry Status

Reading industry literature and customer reviews can shed new light on a prospective technology partner. When deciding, you want to ideally choose an industry-leader or a highly competitive company with a proven track record and known competitive advantages. In some cases, the first-mover, or company that started the industry, is the leader. It has spent more time developing solutions, learning from mistakes, acquiring customers, and building its brand. To find e-commerce literature, we recommend subscribing to e-commerce blogs and newsletters. For customer reviews, check out Capterra.

Funding and Capital

By and large, most of the companies selling an Amazon repricer are startups. Startups come and go. Any Amazon seller or retailer shopping for a repricer should ensure that their technology partner is well-funded and growing. To research this information and much more, search on Crunchbase and AngelList.

Company Size and Personnel

Many companies claim to have an algorithmic repricer; however, as we have already noted, actually supporting one takes teams of technically talented individuals, like software developers, data scientists, and engineers. In addition, many companies offer customer success teams and support agents. You should know whether only a handful of people are available or many. A great starting point for this research is LinkedIn.

Expanding Services

No one can really predict what solutions a company will release next, but you can assess whether the right conditions exist for it. Companies that successfully launch new solutions tend to have funding, good cash flow, technical acumen, top talent, and a solid, if not expanding customer base. You will want a technology partner that can scale with your growth, so conducting this research is worthwhile.

Clientele

Customer reviews and a company’s case studies can reveal whether a technology partner is right for you. Customer reviews often contain unfiltered insights. Although a company’s case studies tend to reflect best-case scenarios, they also reflect the size of the clientele that your prospective technology partner services. Each case study is also a potential contact who can give you a “boots on the ground” perspective of what working with that technology partner is like.

Conclusion

Amazon is the second company to ever reach a trillion dollar valuation. As one of the foremost destinations for consumer searches, it can no longer be ignored.  Every industry it enters, it seems to disrupt, if not dominate, within a very short period of time.

Through this complete reinvention of commerce, Amazon has opened up meteoric growth opportunities for large sellers, retailers, brands, and private labels. While much of this growth is confined to the United States, global markets remain relatively untapped for now. For each of these types of sellers, the best way to capitalize on this growth is to have a technology partner that can scale with your success.

Finding the right technology partner isn’t just critical; it is an indispensable gateway to new profits whether in the United States or abroad. Given that Amazon repricing is a relatively new industry, many of the companies in the space are ambitious startups with limited resources. To ensure your success for the long haul, you will want to choose a partner that will maximize your growth today and evolve with you tomorrow.

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