Originally published here: https://retina.ai/blog/tis-the-season-for-clv/

In our recent blog, we discussed how COVID-19 has changed online shopping, possibly forever. How will these trends impact holiday shopping for 2020?

In CommerceNext’s recent webinar on “Understanding Shopper Psychology as We Get Ready for Black Friday”, panelists and attendees shared stats and projections for the upcoming holiday season.

eCommerce Wish List

As more shoppers go online for everyday purchases, so too will they turn to eCommerce sites for holiday shopping. Between 2018 and 2019, we saw a 13.6% increase in eCommerce sales, from $122 billion to $138.65 billion.

This year, analysts predict that eCommerce sales will increase…

Originally published here: https://retina.ai/blog/covid-19-changed-online-shopping-forever/

During the week of March 15, the search term “toilet paper” reached peak popularity on Google, or a value of 100. For comparison, the value of that search term in February 2020 and again in April 2020 hovered around 2 or 3. It’s likely most of us searched Google for “toilet paper” in a desperate attempt to find some in stock online.

How has online shopping changed since this wave of panic buying in late March? This blog explores trends collected in the Selligent Global Connected Consumer Index. …

Originally published by Mo Messidi, Director of DataOps Engineering at Retina, here: https://retina.ai/blog/delivering-value-from-data-using-dataops/

This is an opinionated guide on how to use DataOps to deliver business value from data. It is a prescriptive fix for organizations struggling to realize expected returns on their data science and analytics investments.

Origins of DataOps

In 2015, Gartner released a report that stated that 60% of data projects in the U.S failed to deliver their desired business outcomes. These failures were attributed to some sort of process failure, a technology failure, or even a people and culture failure. Two years later, in 2017, the failure percentage was…

Originally published here: https://retina.ai/blog/customer-loyalty-during-and-after-covid-19/

Brand loyalty has shifted during the pandemic due to increasing price sensitivity, delayed shipping times, product availability, safety concerns, and more. Consider a product like toilet paper. Before COVID-19, many consumers were very brand loyal, perhaps only buying Charmin Ultra Strong for its superior performance or Cottonelle Comfort Care for its softness.

When the pandemic hit, paper products were one of the first household items to sell out. At the same time, many consumers either lost their jobs or were working less hours or for less pay. …

Originally posted on Retina AI: https://retina.ai/blog/shifts-in-buying-behavior/

Back in March, the US economy all but shut down to help slow the rate of spread of COVID-19. At the time, people felt a shift in their needs and values, and brands were forced to rise to the challenge of meeting those needs almost immediately. Pivots were made to service the consumer with immediate gratification and convenience, and forecasts went out the window.

Even now, as regions begin to ease COVID-related restrictions on brick and mortar stores and the economy is starting to slowly pick up again, some changes in consumer behavior may…

Originally published by Caroline Kratofil here: https://retina.ai/blog/d2c-to-omnichannel/

With so many D2C brands selling on Amazon, Target, and Walmart, should you make the jump to omnichannel? We will discuss the pros and cons and share best practices for expanding your D2C business.

Starting Point

These days, creating a D2C business can be relatively inexpensive. With platforms like Shopify, eCommerce brands can start small with low overhead costs. Plus, new service companies provide support for warehousing, fulfillment, content, call centers, return processing, and more.

New brands can start with limited marketing budgets and acquire loyal customers through new channels like Instagram influencers. …

Originally published here by Emad Hasan, CEO and Co-Founder, Retina.

I read through today’s earnings calls by Tapestry and found it to be very interesting. They mentioned the words profit or profitability 40 times and the word customer 70 times. However, customer lifetime value and repeat purchase rate is mentioned only once. We are seeing luxury businesses suffer from massive decreases in customer-level lifetime value even when new customer growth is up. Another interesting company is Wayfair, which has a ton of scandal surrounding it right now, but also reported profits for the first time ( report).

This gives rise…

A lot of customer service and customer support teams are making the shift to customer success or even customer experience. While the former teams are reactive, the latter help identify customer problems and offer potential solutions.

CLV can help you answer questions like:

  • Who will be the next customer in need of assistance?
  • When will this happen?
  • What future value is at risk from a dissatisfied customer?

Defining customer success

First, let’s define what makes an interaction with a customer successful. Most customer service teams use metrics like SLA, CES, NPS, and CSAT to assess the quality and speed of their solutions. Instead…

Originally posted at https://retina.ai/blog/calculate-roi-build-or-buy/ by Emad Hasan, CEO and co-founder at Retina.

This article explains how to make an ROI case for either building or buying data solutions.

We will start with the concept of understanding value and then define fixed and ongoing investment costs. We will show how value is a function of revenue as well as fixed and variable costs at the individual customer level. Finally, we will connect the two concepts to understand expected incremental value based on an investment.

Figuring out the value of building or buying data solutions data solutions is difficult. An executive at…

Originally published at https://retina.ai/blog/moving-models-to-production/ by Mo Messidi, Sr Data Ops Engineer at Retina.

This article shares the common issues that data teams face when moving their models from development to production systems and how these issues could be avoided.

Problem 1: Mismatched metrics

Let me tell you one of my favorite stories. The story highlights my dear data scientist friend Bob and his journey of converting his machine learning model into production.

Retina AI

Curated by Caroline Kratofil, marketing lead at Retina AI, the customer intelligence partner that empowers you to maximize profitability. https://retina.ai/

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