Name: Trupti Nivrutti Deore

Roll No.: 2K191133

Specialisation: Business Analytics

E Commerce Analytics II

1) Optimizing for E-commerce conversion and user Experience.

Ecommerce optimization is a holistic approach to improving your website and allowing

visitors to easily convert to customers. From navigation and layout to content and product
descriptions, everything on your website should intentionally lead users towards the end goal
- buying your products or services.

Conversion Rate Optimization (CRO) is the practice of increasing the percentage of users
who perform a desired action on a website. Desired actions can include buying a product,
clicking 'add to cart', signing up for a service, filling out a form, or clicking a link.
Conversion rate optimization is important because it enables you to reduce customer
acquisition costs by getting more value from the visitors and users you already have. By
optimizing your conversion rate, you can increase revenue per visitor, acquire more
customers, and grow your business.

It takes a lot to launch and build an e-commerce business. After we determine how you will
get your products, set up your store and start sending visitors to your site with paid
advertising. In an increasingly competitive world of digital shopping, there are a few dos and
don'ts when it comes to encouraging customers to purchase items from your company. This
act is called conversion and it is the most important metric that we will have to keep an eye
on as we plan to develop our business and increase revenue.

And the conversion doesn't just happen: we need to optimize it. This applies whether we are
starting and running the entire program ourselves, or if we are an e-commerce manager or
marketing director who has signed up to hit massive goals and KPIs for the year.
Conversion rate is defined as the percentage of visitors who come to your website and who
complete a desired action.

Here are typical conversions for an ecommerce website:

1. An online sale.
2. A user who adds a product to his cart.
3. A user who adds an item to his wish list.
4. Email records.
5. Actions in social networks.
6. Any KPI that your company considers valuable.

2) Analysing E-commerce customers

Customer analytics refers to a collection of data points that indicate what customers are
interacting with, how, and for how long. Interpreting that data can help you understand what's
resonating with your different customer segments.

All of your marketing initiatives will have some type of customer analysis attached. Think
about your website and these relevant customer analysis examples:

2) Number of daily page views.

3) Number of users who click on a featured homepage offer.
4) Percentage of users who bounce compared to how many stay to visit another page.
5) Average amount of time spent on a web page

Guesswork is rarely an effective marketing strategy. But collecting, analyzing and making
decisions based on the real data attributed to your customers will always work in your favor.

1) Capture, store and organize customer data.

There are many ways to collect data and many tools that offer some form of customer
analysis. But to fully understand and act on that data collection, you need a single place for
data management.

2) Analyse and make decisions with that data.

Once you have all your customer analytics in one place, it's time to implement and act. Use
these data points to identify what is working and what is not working in your marketing
strategy to identify shopping habits and make iterative changes to the customer experience.

3) Analysing products and orders in E commerce

Orders are the financial core of e-commerce and represent, at a macro level, the interaction
between a buyer and an e-commerce site when value, usually money, is exchanged. Products
are purchased within orders. At the technical level, e-commerce orders are transactions
defined as a logical operation on data and are said to have atomicity, consistency, isolation
and durability (ACID) within databases. For the purposes of this chapter and in general
within analytics, the transactions we want to analyse are electronic commerce orders that are
captured, stored and processed in analytical systems.

Ecommerce order management is the back-end process for managing and fulfilling orders
online. This includes everything from order routing and shipping label printing to managing
returns and subscriptions.

Order Management Systems (OMS) provide automation and integration at every step of the
order journey. This enables brands to deliver consistent customer experiences at scale across
all channels. E-commerce order management systems allow operators to manage orders that
arrive from multiple sales channels and leave multiple fulfilment points. It facilitates
automation between service providers and aggregates data within a single interface.

4) Attribution in E-commerce Analytics.

An attribution model is the way you assign credit or value to sales and conversions at various
points of customer contact. It includes all of your digital channels (paid search, viewing,
email, social, organic search, referrals) and the impact each one has on the final conversion.

There are at least five different models that are widely used, and even more depending on
how you define and break them down:

1. First Touch allocates 100% of the credit to the first point of contact on a conversion path.
This is great for understanding how people find you (and the top of your funnel), but if you
hit three other touchpoints before converting, do you really deserve all the glory?
2. Last Touch gives full credit to the last point of contact, no matter how many others have
gone through. Easy to track and configure, but almost universally considered useless today.
Too much is happening beforehand and you don't give notice to the top and middle of the
funnel activities.

3. Linear assigns the same value to each step in the conversion path. If a customer travelled
through four touchpoints before purchasing, they would each get 25%. This is better, each
point is considered and valued, but it tends to overvalue the minor ones and undervalue the
key touch points.

4. The position favours both the first touch and the last, usually giving each 40% of the credit,
while dividing the remaining 20% between the points of contact in between. Obviously, the
model can drastically undervalue the medium, especially on a long road.

5. Time-decay is a simple algorithmic model that gives more credit to the point closest to the
conversion, and less and less as you move away from it. While it still favours the finishing
touch, it compliments every step of the way and as such is the model of choice for many
marketers and business owners.

5) Integrating Data and Analysis to drive E-commerce Strategies.

Integration refers to the process of gathering data to unify it in a common form, structure and
/ or system. As an ecommerce analyst, the integration work you do will be driven largely by
the mission and vision of your analytics team leader. For technical analysis teams that
manage and maintain the infrastructure and technology that supports collection, storage,
processing, modelling, reporting, and visualization, integration work will revolve around data
integration for create new data sets and integration of applications and systems connecting
and uniting techniques. Tools to support analytical business processes.

To stay ahead and be successful, companies need in-depth knowledge of inventory, markets,
user preferences, and buyer behaviour. An e-commerce site produces data, but that data must
be analyzed to obtain the information necessary to make quick business decisions that
effectively move the business forward.

Ecommerce data analytics is used to help companies better understand data and use it
effectively for customer-centric initiatives, including:

1. Personalized marketing
2. Improved conversion rates
3. Omni channel merchandising
4. Store promotions
5. AI enabled commerce
6. Inventory planning
7. Supply Management
8. IOT and real-time visibility
9. Better customer service
10. Reduced cost