HARNESSING ARTIFICIAL INTELLIGENCE FOR A COMPETITIVE …intela.ai/wp...Paper-Harnessing-AI-Analytics-in-Retail-and-E-Commerc… · Intelligence analytics, which uses machine learning

HARNESSING ARTIFICIAL INTELLIGENCE FOR A

COMPETITIVE EDGE IN RETAIL & E-COMMERCE

THREE MACHINE LEARNING ANALYTICS CASE STUDIES JULY 2017

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Copyright © 2017 Anodot Ltd.

STATIC BUSINESS INTELLIGENCE CANNOT KEEP UP WITH TODAY’S

BIG DATA

Retailers and pure-play e-commerce companies face the ongoing challenge to increase sales by

improving conversion rates, reducing cart abandonment and recommending relevant offers to

visitors, among other things. Most companies use Business Intelligence tools to understand their

business, but BI is not designed for the vast, dynamic nature of Big Data, where revenue-impacting

issues like price glitches and integration errors can occur at the touch of a button. Enter Artificial

Intelligence analytics, which uses machine learning to understand the normal behavior of data

metrics in order to alert on anomalies, or business incidents.

This paper explores how several companies have implemented Anodot’s AI Analytics to identify

problems and leverage business opportunities faster. Through these case studies, we will

demonstrate how AI analytics can help companies to:

• Obtain insights in real time, preventing revenue leaks and brand-damage, and enabling

them to capture new business opportunities

• Discover the metrics that matter in an overwhelming sea of data, and Illuminate “data blind

spots”

• Gain a complete picture of business drivers by correlating data from multiple sources

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REAL TIME INSIGHTS FOR FASTER ACTION

CASE STUDY 1: REAL-TIME ALERTS LEAD TO RAPID ACTION ON OPPORTUNITIES AND PROBLEMS

The Challenge

A Fortune 500 Apparel Conglomerate relied on traditional tools like alerts, dashboards, and

statistical analysis software to analyze its data. However, the BI team was having trouble keeping up,

and would often get unpleasant surprises, discovering issues and opportunities long after the

financial or reputation damage was done.

In one case, the company ran out of a hot product in one regional warehouse. Two days later,

the BI team learned that a celebrity had endorsed it on Instagram. If they had known about

the endorsement in real time, the company could have replenished inventory more quickly, or

taken other actions like changing product pricing, or even bundling the hot product with a

slower moving offering.

The Solution

The company implemented Anodot’s AI Analytics, which provides powerful insights based on

machine learning algorithms. The Anodot solution automatically learns the normal behavior

for all of the company’s business data, and provides alerts in real time for any anomaly.

“No one thinks sneaker sales can be that complex until you start looking at all the different

things that drive transactions,” the Data Analytics Director said.

According to this expert, “You’ve got the basics like competitor pricing, seasonal trends, and

advertising conversion rates. Then you get thrown a curve ball like a celebrity wears your

sneakers for the red carpet premier of a major motion picture in London. Website sales start

going crazy in Europe. But, you’re really not sure why, because no one on your team is reading

the online celebrity gossip magazines. At the same time, there is a dramatic drop in sales in

North America. Before Anodot, it would’ve taken us weeks to figure out the cause of purchase

changes in so many different locations, and for so many different products. We would’ve

relied on intuition together with our dashboards. With Anodot, we get real time alerts for



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sales spikes, like in Europe with the celebrity endorsement, or when an impending snow

storm causes a decline in in-store purchases in the Midwest.”

While it may seem trivial for a human being to correlate between these factors, consider that

the retailer sells thousands of products in over a hundred geographic regions. To focus on

each product and its influencing factors at exactly the right time is far beyond any human

capability; only artificial intelligence can highlight which items need attention on time.

Using Anodot has transformed the BI team from reactive to proactive, giving them the tools to

approach brand executives with new opportunities, pointing out glitches that they uncovered,

and making them an invaluable resource to the company. The result has been a steady

increase in revenue based on plugging the company’s leaky funnel and leveraging previously

unnoticed opportunities.

Now that the company has real time knowledge of what is happening in their business, they

will be rolling out automated actions such as inventory orders and pricing changes, based on

relevant alerts from Anodot.

DISCOVERING THE METRICS THAT MATTER; NO MORE DATA BLIND

SPOTS

CASE STUDY 2: QUICKLY DETERMINING THE ROOT CAUSE OF CART ABANDONMENT

The Challenge

A pure-play e-commerce provider had done everything it could to improve its conversion funnel,

from process simplification to design changes to technical upgrades. And yet, from time to time,

conversion would decrease for unknown reasons, or cart abandonment would spike, negatively

impacting revenues.



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While extremely knowledgeable, the data analytics staff did not have access to the solutions

they needed to diagnose these issues. Determining the root cause required extensive digging,

consulting with the development team, and a lot of head scratching.

Despite the multiple BI dashboards and alerts that they had configured, the team had many

data blind spots, preventing them from doing their jobs well.

“We’d be looking for issues on our dashboards, and then something else would come in from

left field and start affecting our results; things that were totally off our radar. We just were not

able to stay on top of the relevant data since it was constantly

changing,” recalled one data analyst.

The Solution

The company started using Anodot to track its business,

application and IT metrics. Immediately, the company started

seeing results, with Anodot pointing out the metrics that

matter, rather than requiring users to determine the questions

to ask.

In one incident, business revenue KPIs dropped dramatically – cart abandonment was up and

revenue was down. With Anodot AI Analytics tracking the full data stack including system

metrics, the analytics team was quickly able to determine that the decline was due to a system

issue. HTTP errors and an increase in open connections had caused a high consumption of

database memory, which in turn drove up a higher rate of ‘HTTP 500 status code - internal

errors.’ This caused customers to leave the site without completing transactions, leading to a

decline in revenue.

According to the client: “Our data analytics team was thrilled that the problem was discovered

in near real-time and solved within a few hours. Without Anodot, we would have been flooded

with alerts but would still not have known where to look for the root cause.”

“Without Anodot, we would

have been flooded with

alerts but still would not

have known where to look

for the root cause.”



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UNDERSTAND BUSINESS DRIVERS THROUGH CORRELATED DATA

FROM MULTIPLE SOURCES

CASE STUDY 3: GLOBAL RETAILER DISCOVERS RECOMMENDATION ENGINE GLITCH WITH ANODOT

The Challenge

A 100-year old company with more than a dozen globally recognized brands had grown over the

years from their traditional brick and mortar stores to a solid combination of web, mobile, and in-

store commerce. To make decisions, the company relied on multiple feeds from numerous

independent sources, but it was difficult for the company’s data analysts to get a complete

picture of the business. Like many, this retailer’s systems were strictly ‘siloed.’

The disparate systems included: CRM systems (e.g. Salesforce.com), web analytics for its

multiple sites (e.g. Google Analytics, Adobe Analytics, Mixpanel, Webtrends), and social

analytics (e.g. Twitter, Facebook), as well as other internal and external sources, such as

weather, customer behavior, website performance, and data related to customer device use

such as location, mobile applications, and type (e.g. Apple, Android).

The Solution

The company sought a solution that could learn and

interpret its complex data in order to utilize their data as an

asset to give them a competitive advantage. They turned to

Anodot’s AI Analytics.

According to the company’s director of digital analysis,

“When we started to really begin to explore the power of big

data, we realized that we needed a tool that would help us

bring data together and optimize revenue. We wanted

“Using Anodot to track data

from multiple sources, we were

able to quickly identify a

problem that could’ve caused a

significant revenue drop.”



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something that pulled everything into one platform that we could use for everything from

shipping to emerging markets. This is why we chose Anodot.”

Using the Anodot solution, this retailer now tracks and finds incidents in more than 260,000

real-time metrics extracted from a variety of internal and external data sources.

With Anodot, the company has discovered and solved incidents daily, citing two specific

examples:

• Identified glitch in recommendation engine that would have otherwise led to a

significant revenue drop. A development update broke the site’s recommendation

algorithm, which meant incorrect offers were being displayed to customers. Upsell

revenue started to slip quickly, but as soon as Anodot alerted on the incident, the team

began rolling out a fix.

“Using Anodot to track all of our data, we were able to fix it on the same day, where

previously it would have taken up to a week and potentially cost us millions in

revenue,” said the company’s director of digital analytics.

• Alerts on competitor keyword bid decisions. Anodot tracks site traffic from search

engines, together with revenue per product, correlated with their competitors’

keywords bids. By doing this, the company immediately knows if product page visits or

revenue is being impacted by its competitors’ bid decisions and can take appropriate

action, for example by raising their own bids as a countermeasure or kicking off a

product promotion.

This company found that with revenue, reputation, and mission-critical business decisions at

stake, having reliable business insights into all of their internal and external data at their

fingertips was critical.

AI ANALYTICS: HOLISTIC VIEW + TIMELY ALERTS As these case studies demonstrate, artificial intelligence brings multiple benefits to retail and

e-commerce analytics. Trying to identify the cause of an incident in a timely fashion using

traditional BI solutions can be like looking for a needle in a haystack, particularly when you’re

faced with interpreting continuously changing data from dozens of different sources. AI

overcomes the inherent latency in traditional monitoring and BI tools, empowering

companies to discover business problems before it’s too late, before they affect revenue or

the company’s brand equity.



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ANODOT’S REAL-TIME AI ANALYTICS Anodot’s real-time AI analytics solution is ideal for retailers, pure-play e-commerce companies

and other data-driven companies. The solution discovers outliers in vast amounts of time

series data and turns this information into crucial insights that can be leveraged to improve

business outcomes. Using patented machine learning algorithms, Anodot isolates issues and

correlates anomalies across multiple data sources in real time, eliminating business insight

latency and supporting rapid business decisions.

● Connects to Any and All Internal or External Data—The Anodot system can connect,

capture, and interpret data feeds directly from all of your different data sources,

without having to pick and choose the important feeds—from web and social media to

weather or competitor information. Available collectors for typical solutions like

Salesforce.com, IBM Websphere, Google Analytics, Adobe Analytics and many more,

make it easy to gain a holistic view of your data.

● Built-in Data Science—Anodot doesn’t require any manual configuration, data

selection or setting of thresholds, so any type of user can benefit, without any data

science expertise necessary.

● Auto Correlation and Detection—Anodot’s algorithms intelligently correlate related

anomalies to avoid alert storms and enable faster root cause analysis, by showing the

full story of each incident across multiple data sources.



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ABOUT ANODOT Anodot illuminates data blind spots with AI analytics, so you will never miss another revenue

leak or brand-damaging incident. Its automated machine learning algorithms continuously

analyze all your business data and alert you in real time whenever an incident occurs, even for

questions you never thought to ask. By detecting the business incidents that matter, and

identifying why they happen by correlating across multiple data sources, Anodot lets you

remedy urgent problems faster and capture opportunities sooner. Over 40% of Anodot’s

customers are publicly traded companies, including Microsoft, Waze (a Google company),

AppNexus, Comcast and many others. Learn more at: http://www.anodot.com/.



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APPENDIX Sample e-commerce/retail use cases with some relevant data sources, metrics and properties that Anodot can track.

Issues/Opportunities Anodot Discovered

Data Sources Metrics Properties Root Cause

Decrease: Conversion rate Transactions Avg time per session Increase: Bounce rate Avg response time

Web Analytics (e.g. MixPanel, GA, Adobe Analytics, CoreMetrics)

Transactions completed, Avg time per session, Bounce rate

Brand, Page Path, Device type, Browser, Region

Changes in CDN config caused dynamic and static content to be fetched from the origin in Hong Kong instead of from the closest server Performance monitoring services

(e.g. CatchPoint, Pingdom, Gomez, Keynote)

Avg response time, Avg dom load

Brand, Page Path, Device type, Browser, Region

Increase: Sales Transactions Winter product lines for men And: Severe weather on the East Coast


Users, Sessions Brand, Page Path, Device type, Browser, Region

Severe weather on the East Coast created higher demand for men’s winter product lines

E-commerce platform (e.g. WebSphere, Demandware, Magento)

Total sales, # of transactions

Brand, Product line, Region

Weather source (e.g. Weather.com, WeatherTrends, accuweather)

Wind speed, Temperature, Rain

Region, City

Decrease: Sales Transactions Sessions Decrease: Price of major competitor Search queries leading to the brand


Sessions Brand, Page Path, Device type, Browser, Region

One of the competitors reduced their price across all product lines by 20%. At the same time, the same competitor increased ad bids on all the major search engines.

E-commerce platform (e.g. WebSphere, Demandware, Magento)

Total sales, # of transactions

Brand, Product line, Region

Competitor price tracker (e.g. Market Track)

Price, Price change Competitor, Product line, Region, Device

Customer acquisition analysis tools (e.g. HitWise)

-Clicks by query Competitor, Product line, Region, Device



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HARNESSING ARTIFICIAL INTELLIGENCE FOR A COMPETITIVE …intela.ai/wp...Paper-Harnessing-AI-Analytics-in-Retail-and-E-Commerc… · Intelligence analytics, which uses machine learning