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Xena Intelligence - Parzenn Partners

Intelligent data analytics for e-commerce sales.

USA, Massachusetts
Market: Internet and IT
Stage of the project: Operating business

Date of last change: 30.03.2020
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Idea

Our solution is to use extensive predictive data analytics to ensure optimum utilization of ad spend on e-commerce portals like Amazon and to develop benchmarks to enhance product offerings and listings on the e-commerce portals. We also believe that small and mid-sized businesses should get free/economical access to such a platform to help them grow while generating the majority of our revenue working with large e-commerce agencies who could potentially replace/reduce/ease their workforce with our tool.

Current Status

It is currently being modeled and developed internally for Parzenn Partners and is being used for its clients. We have also launched a private label brand on Amazon which utilizes this modeling.

We envision Xena to be an easy to use, intuitive platform freely accessible to all small business owners and a one-still-shop destination to not just understand the workings of e-commerce retail space but also an effective guide in helping grow sales through data analytics and in-built machine learning capabilities.

The larger retail agencies and companies can also use Xena to boost their sales and considerably optimize their online ad spend. Xena can also build in capabilities to predict high performing SKUs within a large organization and aid in establishing effective product title and descriptions to optimize organic SEO and search ranking within Amazon.

Xena would be connected through Amazon through it’s API and would be able to collect all the data for its users. Currently this data collection process is being done manually and eventually it would be automated while scaling our offering to more users.

Market

Selling on Amazon has become an expensive proposition. According to Amazon, half of all the sellers on its platform at SMBs (Small and Mid-sized Businesses). On an average, the net products from selling on Amazon (or most of the e-commerce platforms) varies from 10-15% not including overheads and indirect costs including salaries of employees and other fixed costs. The razor thin margins are a result of increasing shipping and fulfillment costs, hefty Amazon selling referral fees, high cost of advertising on Amazon, other fees including storage fees, customer return fees, coupon promotions, etc. This puts heavy strain on small and mid-sized businesses who often seek the support of retail agencies specialized in e-commerce sales to help them figure out the Amazon jungle with its many moving pieces and massive amount of data generated which needs to be efficiently dissected and acted upon. These agencies charge a hefty fee which further drives down the overall profitability of these small business owners who have no option but to seek outside help. With millions of visits on its platform each day, SMBs are forced to sacrifice profitability just so that they can hope to move considerable inventory through Amazon, and would much rather have some sales than not have that additional source of sales.

Xena intelligence hopes to automate a huge chunk of these tasks which are otherwise carried out by ‘e-commerce experts’ and also help them make decisions on pay-per-click campaigns, product listings and inventory management.

Though there are many softwares and tools out there which do one or more of these tasks, none of them have yet to build a machine learning tool to help business owners make decisions on factors like predicting future sales, pricing strategy and advertising strategy. Xena’s algorithm will do the heavy lifting in terms of data analytics and part of decision making and this would go a long way to help SMBs gain profitability through online sales.

Problem or Opportunity

E-commerce is an increasingly lucrative and competitive space. It tends to be overshadowed by big players who have the resources to push their products to potential consumers by spending vast amounts of money on sponsored advertisements and SEO spending. For the small and mid-sized businesses, this means that they are also forced to spend a considerable amount of money in online advertisements which squeeze down their margins. Combined with increasing costs of selling on popular e-commerce platforms like Amazon, Walmart, and E-bay, the small and mid-sized businesses who wish to sell online are left with razor-thin margins at best and often lose money at worst. Xena aims to help these seller optimize their ad spending based on a proprietary algorithm built upon extensive data analytics to better understand the ever-evolving consumer trends.

Solution (product or service)

Our solution is to use extensive predictive data analytics to ensure optimum utilization of ad spend on e-commerce portals like Amazon and to develop benchmarks to enhance product offerings and listings on the e-commerce portals. We also believe that small and mid-sized businesses should get free/economical access to such a platform to help them grow while generating the majority of our revenue working with large e-commerce agencies who could potentially replace/reduce/ease their workforce with our tool.

Competitors

There are many tools out there in the market for Amazon marketplace sellers. We have used many of them for our clients and for our private label brand. The idea for Xena came about as a result of the shortcomings we experienced while using these products for ourselves.

Tools like Sellozo, Quartile, Sellics and PPC Entourage are quite popular and is even showcased in Amazon’s seller profile itself.

Companies like Jungle Scout are also very popular tools used to predict which product niche would sell well on Amazon, but at the end of the day, these predictions are reductive at best and have a huge margin for error.

While the best of them do help in building up ‘self-learning’ campaigns to optimize ad-spend, none of them seem to utilize market data or data fr om other competitors to determine how to best utilize its client’s ad spend. Most of the strategy and decision making on listing descriptions and pricing is still done by ‘experience agents’ in large retail firms who charge an arm and a leg and the results are questionable at best. Perhaps this model would work for a very large client who is just worried about meeting top line sales but not very good for smaller businesses for whom the profitability and cash flow plays a huge role. In both the cases, there is still a large amount of intuitive guesswork by these experts and these experts are quickly to point out any potential failure in their strategy to external factors for which there are many. There is a common adage that it takes a lot fo time for the ‘system’ to work, and that patience is key in e-commerce.

We tend to disagree with this assessment since e-commerce is data rich and there is certainly a lot we can do to utilize huge data to take the guess work out of the picture. We have developed tried and tested algorithms as for campaign strategy, ad-spend and product listing strategy for our clients and for our private label. This is currently done manually with a little help from data modeling. We believe that completely automating this process can help scale our product and helps reach every SMB who looks forward to sell on Amazon out any other e-commerce platform, wh ere the basic fundamentals are the same.

Another factor that the competitors fail to understand is a the ever evolving nature of the e-commerce world. While a certain strategy may be successful for a period of time, more often that not, it cannot be replicated or sustained due to the ever evolving nature of e-commerce algorithms and the ever changing consumer trends.

Understanding, analyzing and simplifying large swathes of data is the only way ahead to sustain long term sustainable growth in the e-commerce jungle.

Advantages or differentiators

Our competitive advantage is very simple - we learn, and keep learning.

While our competitors function on guess work and ‘industry experts’ who charge an arm and a leg, we like to use data to guide the way for our clients. Not just their data, but also the data we collect from the market place and other sources like popular search engines, social media sites and other e-commerce platforms to enable effective decision making for our users without having them spend considerable time and money in understanding the complicated moving parts by themselves.

Intuitive tools are rare in the marketplace, and we hope to be the simplest and the most effective business tool for small businesses.

Finance

We are currently earning a monthly fee from our small business clients ($1500 per customer). We hope to continue this model to maintain cash flows as we grow.

Business model

Xena’s business model is very simple. Our vision is to help small and mid-sized businesses grow profitably and enable them to effectively navigate the complicated and cut-throat online marketplace.

We hope to offer our tool free of cost to small and mid-sized business (until a certain number of products they offer). Once they have stable sales and are in a position to put up more products online, we can charge them a monthly success fee based on a fixed subscription fee and a percentage of the profits.

For the larger companies and huge retail agencies, we can charge a fixed monthly fee depending on the number and size of clients that they have.

Our core value is the quantity and quality of data that we would collect. The more data Xena collects, the more adept it would get in making predictive decisions for its users.

Money will be spent on

Developing tool and marketing.

Team or Management

Risks

We do run a risk of being bought out or taken over by our competitors who do wish to get into the small business market.

Incubation/Acceleration programs accomplishment

Participated in Babson's Blank Center Incubation program during 2017-18
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Idea
Current Status
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Advantages or differentiators
Finance
Invested in previous rounds, $
Business model
Money will be spent on
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Incubation/Acceleration programs accomplishment
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