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These 4 types of website visitors will get you higher about-face rates

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  • User experience
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  • User (computing)

These 4 types of website visitors will get you higher about-face rates

Aleksandra Lazovic
Story by
Aleksandra Lazovic

About two years ago, when I was autograph my thesis in Business Analytics, I had zero clues that my career will bring me into the world of UX. At that point, I was alive in Agenda Business and wanted to use my thesis to help a client make smarter decisions and get more business inquiries (conversions) while advance in agenda advertising.

I’ve revisited this assay and I’ll try to share my allegation the best way I can. It’s apparently annihilation groundbreaking or eye-opening, but it might help some of you shape some studies of your own or help you prove some ideas you had in your work.

This commodity is a part of my thesis “The role of multivariate assay in evaluating KPIs for Agenda Business activities,” done in September 2017. at the University of Belgrade, under the administration of Dobrota Marina, PhD.

Study summary

In this study, the idea was to aggregate 12 months’ worth of data on aggregation behaviors and use the allegation to advance business efforts. The aggregation in catechism is Maxifit, a benefactor of fettle accessories (B2B segment) in the Balkans region — Serbia, Croatia, Bosnia & Herzegovina, Macedonia, and Montenegro.

I’ve calm 12 months’ worth of data in order to accept which users the client should focus on because they show stronger break for conversion.

Objectives

  • Understand which user segments show behaviors accompanying to conversions
  • Understand which channels bring higher-quality visitors
  • Give the client enough advice to help him better invest the Business budget in altered channels and targeting options

Research process

  • Make an antecedent antecedent about the accord amid user behavior and absolute results
  • Collect data about the user behavior
  • Use multivariate assay to action the data
  • Present key allegation and give recommendations

Digital Business is advised to play a ascendant role in today’s business world. The acceptable media is dispatch down, and the shift toward new technologies is inevitable. Consequently, investments in Agenda Business activities are growing daily. That is why it is all-important to make abreast decisions, which will return the investments to the greatest accessible extent.

Maxifit is a fettle accessories distributor. Their main cold is to accept as many business inquiries as accessible through their acquaintance form.

To briefly give some context, Maxifit is a aggregation that is an absolute benefactor of XBody EMS fettle accessories for the Balkans region (Serbia, Croatia, Bosnia & Herzegovina, Montenegro, and Macedonia). Their main goal is to sell as many XBody accessories or open Maxifit brand franchises as possible. 

A group of people appliance with an XBody device that is in front of them.
Training on XBody Electro-Muscle Stimulation (EMS) device

Usually, in e-commerce businesses, it is easy to draw a alternation amid our activities and the results, because we can see direct sales online. In this case, the artefact is very big-ticket and a arrangement needs to be signed before purchase. That’s why our goal is to accomplish as many email inquiries on the website from people absorbed in purchasing the product. That is what we would call a “conversion.” Then the sales staff takes over.

Research model  and hypotheses

The goal of all business activities is to get as many conversions as possible. In order for the about-face to happen, a abeyant chump has to go through the sales funnel. The focus of this study was the top and the middle part of the sales funnel — brand acquaintance and assuming interest.

Visual representation of the accord amid the two hypotheses.
Visual representation of the accord amid the two hypotheses.

H1: User behavior on the website affects the about-face probability

H2: There is a cogent alternation amid the means of accepting users and their website behavior

In this article, I will solely focus on Hypotheses 1, since it is more carefully accompanying to the topic User Experience, while the other one might help agenda marketers.

Collected data, variables, and user Segments

“The most admired users are the ones who cross deeper into the website — they are usually abiding visitors, who come from the search engines, visit assorted pages on the website and have a higher boilerplate visit duration.”

According to antecedent assay (Plaza, 2011), visitors that are cogent the afterward behaviors have greater break that they will become our customers:

  • Spending more time on the website
  • Visiting assorted pages on the website
  • Visiting the website more than once
  • Visiting website from search engines

This is why I’ve chosen to match altered user segments with variables accompanying to the indicators above and see what kind of accord they have.

  • Variables that announce absolute behaviors:
  • Sessions — Number of visits an alone user has on the website
  • Bounce Rate — What is the percent of users who leave the website right after their first page (landing page)
  • Pages per Affair — Number of pages visited during one session
  • Average Affair Continuance — How long does, on average, a user stay on the website during one session

I’ve absitively to test the differences amid (H1a) Desktop and Mobile visitors, (H1b) visitors that came through Facebook ads and search engines, (H1c) new vs abiding visitors, and (H1d) visitors from altered age groups.

User segments that were tested:

  • Age groups
  • Device used — desktop or mobile
  • New or abiding visitors
  • Acquisition approach — Facebook ads or Google AdWords platform
User demographics represented through the number of sessions
Screenshot from Google Analytics assuming graph for number of sessions on the Maxifit website and numbers for other variables.
Screenshot from Google Analytics — Data calm from June 1st, 2016. to May 31st, 2017.

The fun part — study results

H1a: Is there a cogent aberration amid users who come from mobile and desktop devices?

To assay 730 samples, I’ve used the Independent Samples T-test and Mann-Whitney U test for confirmation.

A bar chart that shows boilerplate values of variables for users visiting from Desktop and Mobile devices.

A bar chart that shows boilerplate values of variables for users visiting from Desktop and Mobile devices.
Average values for users visiting from Desktop and Mobile devices

With a statistical acceptation of 1%, the tests have bent the following:

  • Sessions: The data announce that more sessions per day come from mobile devices, with the same budget allocations
  • Bounce Rate: The after-effects claim that a bigger Bounce Rate is noticed with users who come from mobile devices
  • Pages per Session: Users who come from desktop accessories are visiting, on average, a larger amount of pages than users coming from mobile devices
  • Average Affair Duration: Data shows that users on desktop accessories spend more time on the website than users on mobile devices

The assay shows that we could put a bigger focus on the visitors who come from desktop accessories because they show bigger break for conversion.

Question for added analysis: Do users who come on mobile accessories accomplish worse after-effects because the website has a poor user acquaintance on mobile?

H1b: Is there a cogent aberration amid visitors who came to the website through Facebook ads and visitors who came organically, through search engines?

A bar chart that shows boilerplate values of variables for users visiting from Facebook ads and Google search
A bar chart that shows boilerplate values of variables for users visiting from Facebook ads and Google search

Average values for users that came through Facebook and Google Search

With a statistical acceptation of 1%, both tests have bent the following:

  • Sessions: The after-effects show that more people came from Facebook ads than from the search engine
  • Bounce Rate: Users who come from the search engines have far less anticipation of abrogation the website after the first page (they have a lower Bounce Rate)
  • Pages per Session: Users coming from search engines, on average, visit more pages during their affair than users coming from Facebook ads
  • Average Affair Duration: Users coming from search engines are more likely to spend more time browsing the website

Through the data, we see that the visitors acquired organically, through search engines, are more valuable.

The account could be quite simple. Users who come from Facebook ads apparently didn’t hear about the artefact and casework before, at this point, are just accepting to know the brand. On the other hand, people analytic for Maxifit and XBody already know what they are attractive for, so they are more likely to advance to the next step — send an assay to the sales staff.

H1c: Is there a cogent aberration amid new and abiding visitors?

The idea here is to actuate whether people who are abiding to the website are there because they have the ambition of converting.

A bar chart that shows boilerplate values of variables for users who are new and abiding visitors
A bar chart that shows boilerplate values of variables for users who are new and abiding visitors

Average values for new and abiding visitors

With a statistical acceptation of 1%, both tests have bent the following:

  • Sessions: The data shows that there are statistically more new than abiding visitors
  • Bounce Rate: The new users have a greater addiction of abrogation the website after the landing page
  • Pages per Session: Abiding visitors visit more pages during their session
  • Average Affair Duration: Abiding visitors spend more time on the website during their session
  • The after-effects show that abiding visitors show bigger break for conversion.

H1d: Is there a cogent aberration amid age groups?

In this case, due to the limitations of Google Analytics, we had only 272 samples. The age groups are afar in 18–24, 25–34, 35–44 and 45–54. Because of assorted groups, the tests used were ANOVA and Kruskal–Wallis.

A bar chart that shows boilerplate values of variables for users from altered age groups
Average values for altered age groups
Mean plot diagrams for variables Sessions, Boilerplate Affair Duration, and Allotment of New Sessions

With a statistical acceptation of 1%, both tests have bent the following:

  • Sessions: The greatest number of sessions is made by the age group 25–34
  • Bounce Rate: Biggest Bounce Rate is for users aged 45–54
  • Pages per Session: Users aged 25–34 on boilerplate visit the most pages per session, while the age group 45–54 visits the least
  • Average Affair Duration: Data and tests show users aged 25–34 stay browsing the website for the longest period of time
  • Percent of New Sessions: The greatest allotment of new users come from age group 18–24, and the least number of new users are aged 25–34

Based on the numbers, the visitors who are aged 25–34 show the accomplished about-face probability.

Questions for added research: Should we invest more money into targeting users aged 25–34 in the ads? Is the small number of new users in this age due to the fact that a lot of them already have visited the website, so they are advised abiding visitors each time they come back?

Key findings

To make it really short — data that I have calm during a 12 month period shows that desktop users aged 25–34, who are acquired through search engines, who are not visiting the website for the first time have a greater value for the aggregation I was alive for because they show more signs that they are accommodating to send a business assay (make a conversion).

Even though the assay allegation might not be that abominable or groundbreaking for addition alive in business, Business , or UX Design, the goal here was to prove some ideas about targeting users and to have statistical proof that I can later use to absolve decisions at work.

Desktop users aged 25–34, who are organically acquired through search engines, and who are not visiting the website for the first time show behaviors that announce conversions.

Recommendations

Here’s how these allegation shaped my Agenda Business work at my client’s company:

SEO supports the announcement efforts— After accepting people absorbed in your artefact through ads, they might be browsing the web to investigate the product/service. Since the higher affection visitors are coming from search engines, the aggregation has to work on making the website highly ranked for all important keywords.

Invest in retargeting — Visitors who come back to the website are more likely to convert, so invest efforts into exploring all accessible retargeting options to bring them back to the website.

Understand the visitors aged 25–34 — Who are they? Why are they accurately absorbed in your product? How are they altered from other age groups, what advice are they attractive for and how do they use this website to find information?

Focus on both mobile and desktop — It would be just too easy to say to go mobile-first in this case, but the right answer for me would be to make both adventures flawless. In my research, data did show that more new users come from mobile phones, but that more affection visits come from a desktop device. It might be that the first visit on the phone is important to leave the first consequence so that they come back using a desktop device to find out more.

Before taking these recommendations for accepted and advance great budgets into design, development, and advertising, I would advise comparing the business I was researching with your own, blockage out Google Analytics , and seeing if the same patterns in data occur and doing added testing on altered user segments.

Appear April 24, 2020 — 08:16 UTC

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