Trending February 2024 # English Mistakes Commonly Made In A Dissertation # Suggested March 2024 # Top 11 Popular

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Students tend to make the same language mistakes over and over again in academic writing. Taking a careful look at these lists of mistakes that we often encounter may help you to break these habits. Avoiding them will set your writing apart and give it a more polished feel.

If you want to make sure your dissertation doesn’t contain any language errors, you could consider using a dissertation editing service.

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Spelling mistakes

Although spellcheck features catch many spelling mistakes, they cannot be relied on entirely. These words are still frequently misspelled in many theses.

Incorrect Correct

acheive achieve

benifit benefit

concious conscious

definately definitely

dependant dependent

disatisfied dissatisfied

existance existence

focussed focused

heteroskedesticity heteroskedasticity

homogenus homogenous

imediate immediate

labratory laboratory

ocassionally occasionally

payed paid

posses possess

practicaly practically

precedure procedure

publically publicly

questionaire questionnaire

respondant respondent

seperate separate

skedesticity skedasticity

skewedness skewness

specificly, specifically

suceed succeed

therefor therefore

undoubtably undoubtedly

unforseen unforeseen

wether whether

wich which

Word choice

Incorrect Correct Why?

Researches were conducted. Research was conducted. Research is an uncountable noun.

The amount of variables may change. The number of variables may change. Use number with countable nouns (e.g., changes), amount with uncountable nouns (e.g., air).

A MRI, a HR directive An MRI, an HR directive An abbreviation that starts with a vowel sound takes “an.”

The teenagers that The teenagers who Use who with people, that with things

Adjectives

Incorrect Correct Example

Are both similar Are similar Although the two cases are similar, they are not identical.

Politic Political Both economic and political factors were considered.

So called…, factor based… So-called…, factor-based… The so-called experts only conducted factor-based analyses.

Specially Especially The authors were especially interested in inflation pressures.

Capitalization

Incorrect Correct Example

Results Of The Interviews Results of the interviews or Results of the Interviews Additional information is presented in Table 9 (Results of the interviews).

Conjunctions and linking terms

Incorrect Correct Example

First of all, firstly First First, all participants were given a survey.

However … However, … However, most theorists disagree.

Like Such as Northern cities such as Oslo and Helsinki have long, dark winters.

Nouns/noun phrases

Incorrect Correct Example

Insights in Insights into The results offer valuable insights into the problem.

MNC’s, PC’s MNCs, PCs Most MNCs purchase thousands of PCs annually.

One criteria One criterion Although many criteria were considered, one criterion stood out

Research conclusion Research conclusions Chapter 6 contains the research conclusions.

Taxi’s, umbrella’s Taxis, umbrellas Taxis are often full of forgotten umbrellas.

The childs behavior, the parents attitude The child’s behavior, the parents’ attitude Although the child’s behavior was aggressive, her parents’ attitude was very relaxed.

Two analysis Two analyses Several analyses were conducted, but one analysis was most fruitful.

Two hypothesis Two hypotheses This dissertation proposes many hypotheses.

Prepositions/prepositional phrases

Incorrect Correct Example

Besides, Next to In addition to

From…till… From…to… The ages ranged from 18 to 24.

In the light of In light of The test was cancelled in light of the wind.

Per By The participants were grouped by height.

To what extend To what extent It is not known to what extent the subjects were being truthful.

Pronouns

Incorrect Correct Example

A person… his… A person… his or her… A researcher should cite his or her sources.

You… One…

Punctuating numbers

Incorrect Correct Example

10.000,00 10,000.00 The price was exactly $10,000.00.

1960’s 1960s Many styles of music emerged in the 1960s.

Quantifiers

Incorrect Correct Example

A couple of A few, two/three, a handful of A few controversial questions were also included.

A lot of Many, much, several, a great deal of Much time was spent on the test.

Terms used in citations

Incorrect Correct Example

Et al, Et all. Et al. The mutation is thought to be widespread (Han et al., 1999)

Et al. has Et al. have Omar et al. have asserted that the effect is temporary.

Verbs/phrasal verbs

Incorrect Correct Example

Choose yesterday Chose yesterday The project manager chose her team before planning began.

Divide in Divide into This dissertation is divided into seven sections.

Lead yesterday Led yesterday Although the Liberals currently lead in the polls, the Conservatives led last week.

Make a photo Take a photo The time was set to take photos at 20-second intervals.

Send yesterday Sent yesterday The email was sent to all employees.

Words that are commonly confused

Following these tips will help you to improve your written academic English in general. The next step is to fine-tune your writing depending on whether you are using American, British, or Australian English! A grammar checker can also help you automatically fix mistakes you may have missed after proofreading.

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How Red Hat Made Money In 2010

The past twelve months have been good for Linux vendor Red Hat (NYSE:RHT) as the company grew revenues and income while expanding its product offerings in the cloud and virtualization space. But how did Red Hat grow while others staggered?

During Red Hat’s fiscal 2010 earnings call on Wednesday, Red Hat’s CEO and CFO detailed the steps their company took to leverage open source software to make millions.

For the fiscal year ended Feb. 28, Red Hat reported revenues of $748 million, a 15 percent year-over-year increase from Red Hat’s total revenue in 2009. Net income totaled $87.3 million or $0.45 per share, improvement of 11 percent over Red Hat’s earnings for 2009.

For the fourth quarter of Red Hat’s fiscal 2010, the company reported revenues of $195.9 million, an 18 percent year-over-year increase. That contributed to net income of $23.4 million or $0.12 per share — an increase of 46 percent over the $16.0 million Red Hat reported for the fourth quarter of fiscal 2009. Minus one-time charges, Red Hat’s earnings rose $0.19 per share, ahead of Wall Street estimates of 0.16 per share, according to Thomson Reuters.

Looking ahead, Red Hat CFO Charlie Peters offered full-year 2011 guidance for revenue in the range of $835 million to $850 million, which would represent an annual growth rate of 12 to 14 percent. On a quarterly basis, Peters forecasted first-quarter fiscal 2011 revenue of $202 million to $204 million.

There are a number of reasons why Red Hat has grown its revenue over the last year and why its executives are optimistic that growth will continue for its next fiscal year. Red Hat CEO Jim Whitehurst noted during the company’s earnings call that it has been expanding outside of the company’s core market verticals, which include telecom, financial services and government.

“In industries such as logistics, oil and gas, pharmaceuticals, transportation, travel services, retail and energy, we experienced over 150 percent growth within these mainstream verticals in our top deals compared with last fiscal year,” Whitehurst said.

Another key area targeted by Red Hat over the last year has been going after free Linux users in a free-to-paid conversion program. Moving users from free to paid is an initiative that Whitehurst first discussed in Red Hat’s third-quarter fiscal 2009 earnings call as a key initiative for growth in Linux. Over the course of fiscal 2010, Whitehurst said that Red Hat saw good results from its conversion program during every quarter.

“In the fourth-quarter 2010, we had several six-figure deals that included a significant free-to-pay component,” Whitehurst said.

While Red Hat is actively going after free-to-paid Linux users, however, the company conceded that it actually lost one of its top renewal opportunities during the fourth quarter when an unspecified customer went from paid to free.

“The motivation was, I think, one of principally cost, so they found a free alternative they were going to try for a while,” Whitehurst said. “As you may recall from our previous quarter results, we have had over the last 3.5 or so years maybe two-three customers who have done the same thing. Usually in a period of a year, and sometimes even less than that, we are able to bring that customer back. So we are still hopeful this customer will come back.”

KVM virtualization technology is also a key driver for Red Hat’s growth. Red Hat recently announced that IBM had chosen Red Hat Enterprise Virtualization for the new IBM cloud. The move is one that Whitehurst noted will help to create a different ecosystem for KVM than the one generated by the Xen virtualization technology that Red Hat and IBM first backed.

“One of the issues … which has made it difficult to make progress in the enterprise is there are so many flavors of Xen out there,” Whitehurst said. “So I think it is important IBM stood up and said, ‘We are standing behind Red Hat’s KVM. We are not spinning our own. We are keeping this thing together. We are keeping the ecosystem together.’ So there is kind of one enterprise implementation of KVM and I think that is important strategically long-term for us and for KVM.”

While Red Hat is growing into new verticals and with the help of virtualization, Whitehurst isn’t specifically targeting Microsoft Windows as the primary target for competitive replacement.

“In general, the key for us is to catch people as they are moving from Unix to Linux rather than having them move to Windows,” Whitehurst said. “So while certainly actual migrations are important, the bigger strategic field of battle and part of our mainstream adoption effort is to make sure when people move mainstream customers from Unix they are moving to Linux and not to Windows.”

Sean Michael Kerner is a senior editor at chúng tôi the news service of chúng tôi the network for technology professionals.

The Worst Mistakes When Buying A Smartphone: You Will Regret It!

Making the wrong choice for your smartphone’s storage capacity will probably cost you money. It will not last for a long period and you will get frustrated having to periodically delete content, and you will probably regret your choice.

How much storage space should a smartphone have in GB?

Although it might seem difficult, the question is not. The publication date is crucial because, if you read an article from 2024, it’s likely to contain information that is now wholly irrelevant. We’re talking about the 2023 year.

Choose a 128 GB option if you want your smartphone to last for a long time without experiencing space issues. If money is tight, you’re probably looking at 32 or 64-GB options.

64 GB is preferable but still probably not enough. 64 GB may not be enough for most users because apps are getting heavier and cameras are producing better-quality images. This puts pressure on the operating system.

The appropriate response to this question is 128 GB. With that much space, almost any circumstance will be accommodated. Thousands of photos, hundreds of apps, and files can all be stored without any issues. There will be some cleaning from time to time, but nothing that will make you suffer every few days.

Put a little more money into it to avoid regret

It’s simple to state that a device with 128 GB will cost more money. Instead of focusing on immediate savings, think about the future. Additionally, the price difference is generally not too big for cheap mobile phones.

Gizchina News of the week

Looking at the smartphones on Amazon, we found the Redmi 10A, which has a 64 GB version for 138 dollars and a 128 GB version for just 14 dollars more.

If you know how to search and wait, the difference is typically very small for preventing future storage issues. The worst of Android‘s annoying features might be its constant storage shortage.

The smartphone’s size and weight matter

You should pay attention to the size and weight of the smartphone you are buying. Some users have small hands and can not handle large smartphones with ease. Also, the weight of the smartphone is a very important aspect that you should consider and to a high degree, you will regret buying a heavy phone that can be problematic when using it, especially while lying down.

Some users are really struggling when using these giant smartphones. As example, the iPhone Pro Max version, the Galaxy S Ultra models, and the Xiaomi Ultra, to name a few. These smartphones weigh over 225 grams and the smaller one of them has 160.7 x 77.6 x 7.9 mm dimensions.

Fortunately, there are still smartphones on the market that combine both large enough screens and large enough batteries without having a very large weight and size that could ruin the experience. Just look at the specifications of the smartphone and especially the dimensions and weight. You will feel much better using the smartphone at home and outside, especially if you have small hands or small pockets. In addition, the smartphone will suit the pocket of anything you are wearing and will not protrude too much like with giant phones.

Analyzing Data Made Effortless Using Chatgpt

Introduction

To learn more about the development of generative models with hands-on experience, join us at the ‘Natural Language Processing using Generative Models’ Workshop at the DataHack Summit 2023. Attending DataHack Summit 2023 will be a game-changer for you. The workshops are designed to deliver immense value, empowering you with practical skills and real-world knowledge. With hands-on experience, you’ll gain the confidence to tackle data challenges head-on. Don’t miss out on this invaluable opportunity to enhance your expertise, connect with industry leaders, and unlock new career opportunities.

Why Prompts are Critical in ChatGPT?

I realized that prompts are very critical in order to make use of ChatGPT to its full potential. Even Though ChatGPT is capable of performing any task, in order to make use of it to its full extent, we need to provide the right and detailed prompts. Without the exact prompts, you will not be able to get the desired results.

I am running the experiment to see if ChatGPT can really make sense out of the dataset. I know that ChatGPT can provide me with the code snippets of certain tasks.

For example, given a prompt “help me with the code snippet to check for outliers”. ChatGPT provided me with a code snippet to check and identify the outliers. But can a ChatGPT help me answer the questions such as determining the columns that contain outliers in the dataset? or what is the correlation coefficient between the target variable and features?

In order to answer these questions, ChatGPT has to analyze the specific columns in the dataset and do the math to come up with the answer.

Fingers crossed!

But it’s really interesting to see if ChatGPT can do the math and provide me with the exact answers to the questions. Let’s see!

Exploratory Data Analysis (EDA) Using ChatGPT

Let’s try some of the prompts, EDA using ChatGPT:

Prompt 1:

I want you to act as a data scientist and analyze the dataset. Provide me with the exact and definitive answer for each question. Do not provide me with the code snippets for the questions. The dataset is provided below. Consider the given dataset for analysis. The first row of the dataset contains the header.

Prompt 2:

6,0,3,”Moran, Mr. James”,male,,0,0,330877,8.4583,,Q

Prompt 3:

How many rows and columns are present in the dataset?

Prompt 4:

List down the numerical and categorical columns

Prompt 5:

Check for NANs present in the dataset? If yes, print no. of nans in each column.

Prompt 6:

Are there any outliers in the dataset?

Prompt 7:

Name the columns that contain the outliers. Provide me with the exact answer.

Prompt 8:

What are the significant factors that affect the survival rate?

Prompt 9:

Determine the columns that follow the skewed distribution and name them.

Prompt 10:

Generate meaningful insights about the dataset.

Such cool stuff 🙂 As you can see here, ChatGPT provided me with a summary of valuable insights and also the important factors that might have affected the survival rate.

Conclusion

Impressive! ChatGPT is able to generate meaningful insights in no time. My experiment is successful. And ChatGPT lived up to my expectations.

To learn more about the development of generative models with hands-on experience, join us at the ‘Natural Language Processing using Generative Models’ Workshop at the DataHack Summit 2023. Attending DataHack Summit 2023 will be a game-changer for you. The workshops are designed to deliver immense value, empowering you with practical skills and real-world knowledge. With hands-on experience, you’ll gain the confidence to tackle data challenges head-on. Don’t miss out on this invaluable opportunity to enhance your expertise, connect with industry leaders, and unlock new career opportunities. Register now for the DataHack Summit 2023!

Related

Air Pollution Has Made The Covid

In the 50 years since the inception of Earth Day, we’ve had some big wins for the planet—and, consequently, for our health. Chief among those was the 1970 Clean Air Act, which led to the development of air quality standards and decades of increasingly cleaner air.

However, many places still suffer from unhealthy air and air pollution may be slowly creeping back. And now, it appears that low air quality is putting people at greater risk of dying from COVID-19. At least three recent studies have connected high levels of air pollution exposure to increased risk of death from the virus. “COVID-19 seems to be affected by air pollution,” says John Balmes, a spokesperson for the American Lung Association and a professor of medicine at the University of California, San Francisco. “Air pollution is a risk factor both for getting the infection and then going on to more severe disease.”

Air pollution kills an estimated 5.5 million people every year normally, and many of the respiratory and cardiovascular conditions it causes may make for more COVID-19 cases. As Balmes explains, air pollution both lowers our defenses to viral infections, making us more likely to get sick, and renders our lungs more vulnerable to life-threatening complications.

Earlier this month, Harvard researchers announced the first nationwide study investigating air pollution and risk of death from COVID-19. The team used county-level data, covering 98 percent of the U.S. population, to draw connections between exposure to fine particulate matter—tiny soot particles that arise largely from diesel- and gas-burning vehicles—and the likelihood of dying from the virus. They adjusted the statistical analysis to account for variations in factors like population size, number of people tested for the virus, and prevalence of obesity and smoking. The researchers found that on a long term basis, an increase in the average concentration of particulate matter of one microgram per cubic meter led to a 15 percent higher death rate from the new coronavirus.

The authors write that, based on this relationship, if Manhattan had a slightly lower soot concentration—just one microgram fewer per cubic meter—there would have been 248 fewer deaths by April 4 this year. This relationship isn’t surprising given the abundance of scientific evidence for the detrimental impacts of fine particulate matter, says Aaron Bernstein, director of the Center for Climate, Health, and the Global Environment at the Harvard T.H. Chan School of Public Health, who wasn’t involved with the study.

In another recent study, Yaron Ogen, a geoscientist at Martin Luther University Halle-Wittenberg in Germany, used satellite-based data on the pollutant nitrogen dioxide, which can contribute to particulate matter and ozone formation, and data on COVID-19 fatalities to analyze this connection in Italy, Spain, France, and Germany. Ogen found that fatality hot spots, accounting for 78 percent of deaths, were concentrated in five regions, four of which were in northern Italy and one which surrounded Madrid, Spain. The five areas have concentrated air pollution, featuring the unfortunate marriage of heavy emissions and valley geography that tends to clump together pollutants.

In another new paper in Environmental Pollution, researchers describe how the body’s response to the virus is eerily similar to that of air pollution. The paper focuses on two regions in northern Italy—Lombardy and Emilia Romagna, which sit in a valley that tends to accumulate air pollution. At the same time, these regions produce a lot of emissions from industry, making it the most polluted area of Italy and among the most polluted parts of Europe. This valley has also had exceptionally high mortality from COVID-19—about 12 percent of infected patients die, compared to an average of around 6.4 percent globally.

It’s well-known that long-term pollution impairs our upper airways, making us more susceptible to chronic respiratory conditions. But, in addition to that, the Environmental Pollution paper suggests a more specific mechanism that may be exacerbating COVID-19 mortality in polluted regions. Dario Caro, an environmental scientist at Aarhus University and author of the study, explains that both air pollution and COVID-19 cause an increase in cytokine-related inflammation—essentially an overreaction of the immune system when trying to defend itself from toxins and viruses. “The importance of the paper is the correlation we found from the actions of pollution in the immune system and the actions of the virus,” says Caro. “Because inflammation of cytokines is the first step to die from coronavirus, we can say that this step for people living in a polluted area is already done.” Caro adds this is true even for young and otherwise healthy people. (It’s important to note, however, that both of the European studies mentioned here are based on correlations, and therefore may not account for other factors affecting COVID-19 mortality in the pollution hotpots.)

As an article in Science explains, if you don’t fight off the initial phase of COVID-19 infection, it moves deeper in the lungs. There, white blood cells release cytokines, proteins that carry immune signals, to fight off the virus. This can lead to a buildup of fluid and pneumonia-like symptoms. Sometimes the body’s response produces tons of cytokines that instruct immune cells to destroy healthy tissues, in what’s called a “cytokine storm,” which can lead to a cascade of effects on the body ultimately ending in death.

In other words, both the virus and air pollution can injure the lungs in essentially the same way. If our lungs are already damaged from breathing in soot for years, that first step of respiratory destruction wrought by the virus is already there. “If you already have a foundation of a cytokine response to air pollution, and then you throw in a virus that causes a very brisk cytokine response, it’s understandable how air pollution could increase the risk of [disease] progression,” says Balmes.

While many of these early studies on the air pollution-COVID-19 connection are not yet peer-reviewed, including the Harvard study, they are consistent with existing literature on air pollution and disease, says Balmes, who was not involved in the research. He adds that while, to some extent, this information can help us understand where to allocate healthcare resources, those areas are already known: frontline communities that disproportionately face health risks due to greater exposure to air pollution. “We already know where to target those [health] resources,” says Balmes. “The urban counties that have increased deaths due to COVID-19 are the counties that have communities of color and low socioeconomic status, and those are exactly the communities that are most vulnerable … Poverty brings with it a number of risk factors that put people in those communities at greater risk for COVID-19.”

Meanwhile, the Trump Administration continues to roll back protections on pollution. Recently, those changes have occurred quietly as the nation focuses on the pandemic; the newest moves include lowering vehicle fuel efficiency standards and removing limits on the amount of mercury, a neurotoxin, that power plants can release. “Even during the pandemic, there have been several moves by the EPA to further worsen our air quality,” says Bernstein. “I’m not sure now is the time to take measures that would potentially worsen air quality in the United States.”

12 Common Mistakes With Customer Analytical Models

Optimise your customer analytics by getting the models right

Customer analytical models can deliver huge value for companies that invest in them to improve their sales and marketing activities. But even well-known big brands can get it wrong when designing, implementing and operating these models. From Barclays to John Lewis, Cineworld to Pizza Express, businesses across all sectors are benefiting from the use of customer analytics. These days, it is unusual to find a company that does not analyse customer data, even in its simplest form. Customer analytics may fall under business intelligence, marketing operations, finance or even customer support, but wherever it lies it will have the potential to improve the optimisation of sales and marketing functions.

Companies often want to know which products or services specific customers are most likely to purchase, which customers need a nudge to help them to complete a sale and which customers are most likely to leave them. When used intelligently the results that customer analytical models generate have a direct and quantifiable impact on the revenue and profitability of a company. Given this, one would expect that the development and operation of customer analytics would be second-nature to businesses, and a well-established methodology. At Intilery however we often find that companies have little or no use of analytical models within their sales and marketing functions. In addition, where models have been implemented, common mistakes are evident.

Areas for focus

Typically, there are three areas which need the most attention (not accounting for having no model at all).

The planning, design and definition of the models.

The deployment and operation of the models.

The refinement and lifecycle of the models.

We’ll look at the mistakes I see across these three areas:

1. Setting the wrong customer value

The second most utilised customer analytics model is the one most often designed incorrectly. The most common design flaw in the definition of the retention model is the valuation of the customer (sometimes referred to as CLV – Customer Lifetime Value). Consultants often encounter CLV definitions that do not represent true lifetime value, rather a current or recent customer valuation.

Utilising the value attributed to a customer, a retention model may assign a deep discount or a margin-heavy offer to a customer who has only recently become a high-value customer and is likely to churn (either churn completely by leaving or churn to a lower value-level).

Conversely the retention model attributed via the customer value may offer too shallow an offer or incentive to customers likely to churn who were previously high-value customers.

“The retention model should look at the value the customer has previously generated for the company and provide appropriate offers/incentives to the customer to either bring them back to their historically high level of value or stop them churning.” The problem is that by looking at

The problem is that by looking at recent activity or overly averaged values, the wrong or inappropriate offers and incentives may be applied to the customer. Instead, the retention model should look at the value the customer has previously generated for the company and provide appropriate offers or incentives to the customer, either to bring them back to their historically high level of value or prevent them from churning.

2. Not utilising customer value propensity

The second issue with valuing a customer for retention is not taking into account the customers’ propensity to increase/decrease in value, by only looking at current value and/or past value. If this is the case, you limit your model to events that have happened and not events that could happen. 3. Ignoring the social value of a customer The third issue with retention modeling is not taking into account the social network value of the customer. If a customer leaves, or does not receive the service or incentives they expect (yes some customers do expect incentives), then the customer may broadcast this on their social network. Taking into account a customer with a very well connected (virtual or physical) network, you may wish to increase the value of a customer and incentivise accordingly for retention.

3. Not retaining customers all of the time

Another issue with retention modeling is that they are often only run at the beginning of the month to identify customers likely to churn within that month (or any other given period).

To be effective the retention model needs to operate in real-time identifying customers and visitors that are likely to churn and applying the required action to prevent it. Churn triggers are used to identify customers that are most likely to churn. These could include; a product being out of stock, a late delivery, a slow loading page, very few (or no) search results or simply gesturing to leave the page.

For a retention model to be effective, the model must include periodic (daily, weekly or monthly) analysis of churn detection along with real-time churn-triggers. Using these together it is possible to apply offers/incentives or other actions to retain customers and more importantly do so in a cost-effective and margin protecting way.

4. Ignoring seasonal variations

Another common error is developing a retention model that doesn’t take into account seasonal variations, or using a single retention model all year. Customers behave differently according to the season and according to seasonal habits. An obvious example is the increase in browse or spend at Christmas, but what about other calendar events? All variations should be accounted for. Also, products and services may have seasonal variations – such as buying cycles or budgets – and companies may have unforeseen variations due to unpredictable trends.

Whatever the seasonal variation, retention models should be careful to incorporate these into the design.

Segmentation Models 5. Lack of granularity

Segmentation models are the most implemented model across all companies and industries, and yet often the least used. Typically, this model will only place customers into high-level segments and built around value, product, basic behavioural data and sometimes geo-demographic segmentation. The issue with this approach, whilst valuable, is that it does not empower companies to deliver ongoing actions. Instead, it drives businesses to shape themselves around the customer segments and shape their offerings for them. For example; a company discovers it has a noticeable percentage of older customers and therefore develops specific services/products for that customer segment.

The issue with high-level segmentation is that it doesn’t take into account the detailed segments, where segments may contain a small number of customers or even a segment of one customer (known as one-to-one marketing). Detailed segmentation allows a company to take action (often in an automated way) on the various individual behaviours of its customers. The results of of this type of segmentation are much more useful and clear. For example, a detailed segment in the travel industry could show how each specific customer behaves; the number of searches before booking, the prominent day of the week for engagement, the seasonal variation in browsing behaviour, the likelihood that a customer “surfs for vouchers” before completing or a change in the type of service/product the customer views/purchases.

Examining customers across detailed segments enables companies to take specific actions to change or influence behaviours, such as, targeting customers with offers when they are most likely to be receptive or delivering an upsell incentive for a customer who has dropped down to a lower than usual price-point. The key learning is that by deploying detailed segmentation it is possible to target specific actions rather than shape your company around a few high-level customer segments.

Channel Migration Models 6. Migrating customers for the wrong Reason

Companies often devise methods to migrate customers from one channel to another, for both marketing/sales communications and for customer services (sales and service). The mistake here is to do this for the wrong reason or at the wrong time. Companies will try and lower the cost of managing a customer by migrating them from a higher-cost channel to a lower-cost channel, the operational cost for managing customers may be reduced, but this could greatly reduce the lifetime value of the customer if the migrated channel is not sufficiently sales focused. Also a company may migrate customers from social to email, or from branch to online-chat. Again the operational cost of communications may be reduced but not taking into account the sales effectiveness of the new channel could result in lost sales.

Activity Optimisation Models 7. Selling instead of helping

Companies can model the lifecycle of a customer at an engagement level to ensure that each customer has their particular needs met, though often a company will only look at the sales category of actions to try and sell the next product or service to them. While this model is useful and can be used to forecast revenue, it fails to capture the bigger picture.

The next action a customer needs is dependent on many individual and personal factors. Companies should design a number of actions that can be applied (with personalisation) to satisfy the customer and promote customer loyalty. More worryingly, only looking at the sales channel and bombarding customers with constant messaging about products or services they can buy could actually disengage customers completely. Intilery recently worked with a client that had succesfully increased sales in the short-term but had not been able to see the long-term damage it was doing to customer loyalty and retention.

“A well designed model will take into account all possible needs of a customer and communicate solutions to the customer at the right time”.

For example: –

Informing a customer about the company’s app and its benefits

Tell the customer about different ways they can get in touch

Provide details of other channel services, e.g. physical store location or opening hours

Collect further personal details or preferences (but clearly explain why)

Ask the customer for a referral (perhaps with incentives)

Show the customer how to share their purchase/booking to help validate purchasing decisions Know when NOT to communicate with a customer (for a period of time)

Strategic Mistakes 8. Working in silos

Another common mistake is not gaining adequate internal support or buy-in from across the business. Stakeholders from all areas of the business should be involved with the design and operation of a customer analytics model. We recommend that when planning this type of model that you use a RACI matrix for identifying and involving stakeholders.

Why involve other areas of the business?

Ask yourself:-

Can the business operationally support outputs and actions of the model?

Will there be operational costs that need to be budgeted for?

Will customer support need to implement new policies and practices?

Does marketing need to work with new key messages?

Will sales need to adjust revenue targets?

Do new KPIs need to be setup to ensure the model has buy-in longevity?

9. Treating new customers like everyone else

New customers must be treated very carefully, they top-up the customer base and directly impact churn levels. A new customer is the most receptive to offers and cross-sell, but is also the most likely to churn. Reasons for failure are often categorised as too much or alternatively incorrect engagement. Getting this right is key to a long-lasting and profitable relationship with customers. One approach is to simply not contact new customers for a period of time as the behavioural profiles of new customers does not usually reflect their long-term behavior. While this will improve the churn rate of new customers it will affect the bottom line. Instead, a better approach is to design a welcome programme that utilises next-action analysis, detailed segmentation and of course seasonal variation.

The actions that are taken for new customers should be unique and personalised to every individual customer, this may mean that for certain new customers no contact is made at all, for others a full suite of engagement activity, website personalisation, offers and cross-sells are applied.

10. Not refreshing models

As part of ongoing operations, a typical mistake is not refreshing the customer analytical models. Few companies revisit and analyse the ongoing effectiveness of their customer models. Failure to do so depletes the effectiveness and efficiency of the models and can lead to less profitability and increased churn.

As time passes, the ability to gauge the effectiveness of the analytical models increases, therefore companies should regularly assess their models. A variety of assumptions will have been made following the design and deployment of models, when it was simply not possible to analyse or predict the outcomes or understand the customer environment. Taking the time to periodically review your models will allow you to test your assumptions against new real-world data.

Also important is the changing landscape of the business, as company strategies, operations and marketing activities change, the structure, desired outcomes and operations of the analytical models may also require updating. Taking the time to update these to match the direction of the company will ensure strategic alignment.

11. Testing the wrong way

This is commonly caused by uncontrollable environmental factors or over reaching on causation/correlation. The most effective way to test customer analytical models is to test them over a number of business periods, whilst applying statistical methods, and finally simply asking the question “does this make sense”.

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