Overview of Quantitative Data Analysis Methods in SPSS

Analytical thinking in marketing is critical. If marketing is both art and science, the numbers play a big role in the science of marketing. In our Tourism Marketing and Management programme, we study analytical thinking in many courses. One of those is our Practical Tourism Research course. During the course, our students study big data, survey research, online data sets, experimental research and sensor technology as a source of quantitative data. Our main data analysis software is SPSS.

To help our students learn data analysis methods in SPSS, I have collected (From SPSS manual) functionalities and use examples for most common data analysis methods in SPSS. This provides a one-page overview of different data analysis methods and helps to find the correct one for different use cases. Hopefully, the reads of this blog will find this helpful!

SPSS Analyze path

Functionality

Example

Descriptive Statistics

Analyze -> Descriptive statistics -> Frequencies Provides statistics and graphical displays that are useful for describing many types of variables, how many of what are there in your data. The Frequencies procedure is a good place to start looking at your data. What is the distribution of a company’s customers by industry type? From the output, you might learn that 37.5% of your customers are in government agencies, 24.9% are in corporations, 28.1% are in academic institutions, and 9.4% are in the healthcare industry. For continuous, quantitative data, such as sales revenue, you might learn that the average product sale is $3,576, with a standard deviation of $1,078.
Analyze -> Descriptive statistics -> Descriptives Displays univariate summary statistics for several variables in a single table and calculates standardized values (z scores). Variables can be ordered by the size of their means (in ascending or descending order), alphabetically, or by the order in which you select the variables (the default). If each case in your data contains the daily sales totals for each member of the sales staff (for example, one entry for Bob, one entry for Kim, and one entry for Brian) collected each day for several months, the Descriptives procedure can compute the average daily sales for each staff member and can order the results from highest average sales to lowest average sales.
Analyze -> Descriptive statistics -> Explore The Explore procedure produces summary statistics and graphical displays, either for all of your cases or separately for groups of cases. There are many reasons for using the Explore procedure–data screening, outlier identification, description, assumption checking, and characterizing differences among subpopulations (groups of cases). The exploration may indicate that you need to transform the data if the technique requires a normal distribution. Or you may decide that you need nonparametric tests. Look at the distribution of maze-learning times for rats under four different reinforcement schedules. For each of the four groups, you can see if the distribution of times is approximately normal and whether the four variances are equal. You can also identify the cases with the five largest and five smallest times. The boxplots and stem-and-leaf plots graphically summarize the distribution of learning times for each of the groups.
Analyze -> Descriptive statistics -> Crosstabs The Crosstabs procedure forms two-way and multiway tables and provides a variety of tests and measures of association for two-way tables. The structure of the table and whether categories are ordered determine what test or measure to use. Are customers from small companies more likely to be profitable in sales of services (for example, training and consulting) than those from larger companies? From a crosstabulation, you might learn that the majority of small companies (fewer than 500 employees) yield high service profits, while the majority of large companies (more than 2,500 employees) yield low service profits.

Compare Means

Analyze- > Compare means -> Means The Means procedure calculates subgroup means and related univariate statistics for dependent variables within categories of one or more independent variables. Optionally, you can obtain a one-way analysis of variance, eta, and tests for linearity. Measure the average amount of fat absorbed by three different types of cooking oil, and perform a one-way analysis of variance to see whether the means differ.
Analyze- > Compare means -> One-Sample T Test The One-Sample T Test procedure tests whether the mean of a single variable differs from a specified constant. A researcher might want to test whether the average IQ score for a group of students differs from 100. Or a cereal manufacturer can take a sample of boxes from the production line and check whether the mean weight of the samples differs from 1.3 pounds at the 95% confidence level.
Analyze- > Compare means -> Independent Samples T Test The Independent-Samples T Test procedure compares means for two groups of cases. Ideally, for this test, the subjects should be randomly assigned to two groups, so that any difference in response is due to the treatment (or lack of treatment) and not to other factors. This is not the case if you compare average income for males and females. A person is not randomly assigned to be a male or female. In such situations, you should ensure that differences in other factors are not masking or enhancing a significant difference in means. Differences in average income may be influenced by factors such as education (and not by sex alone). Patients with high blood pressure are randomly assigned to a placebo group and a treatment group. The placebo subjects receive an inactive pill, and the treatment subjects receive a new drug that is expected to lower blood pressure. After the subjects are treated for two months, the two-sample t test is used to compare the average blood pressures for the placebo group and the treatment group. Each patient is measured once and belongs to one group.
Analyze- > Compare means -> Paired Samples T Test The Paired-Samples T Test procedure compares the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case and tests whether the average differs from 0. In a study on high blood pressure, all patients are measured at the beginning of the study, given a treatment, and measured again. Thus, each subject has two measures, often called before and after measures. An alternative design for which this test is used is a matched-pairs or case-control study, in which each record in the data file contains the response for the patient and also for his or her matched control subject. In a blood pressure study, patients and controls might be matched by age (a 75-year-old patient with a 75-year-old control group member).
Analyze- > Compare means -> One-Way ANOVA The One-Way ANOVA procedure produces a one-way analysis of variance for a quantitative dependent variable by a single factor (independent) variable. Analysis of variance is used to test the hypothesis that several means are equal. This technique is an extension of the two-sample t test.

In addition to determining that differences exist among the means, you may want to know which means differ. There are two types of tests for comparing means: a priori contrasts and post hoc tests. Contrasts are tests set up before running the experiment, and post hoc tests are run after the experiment has been conducted. You can also test for trends across categories.

Doughnuts absorb fat in various amounts when they are cooked. An experiment is set up involving three types of fat: peanut oil, corn oil, and lard. Peanut oil and corn oil are unsaturated fats, and lard is a saturated fat. Along with determining whether the amount of fat absorbed depends on the type of fat used, you could set up an a priori contrast to determine whether the amount of fat absorption differs for saturated and unsaturated fats.

Compare Means

Analyze- > Compare Means-> Bivariate Correlations The Bivariate Correlations procedure computes Pearson’s correlation coefficient, Spearman’s rho, and Kendall’s tau-b with their significance levels. Correlations measure how variables or rank orders are related. Before calculating a correlation coefficient, screen your data for outliers (which can cause misleading results) and evidence of a linear relationship. Pearson’s correlation coefficient is a measure of linear association. Two variables can be perfectly related, but if the relationship is not linear, Pearson’s correlation coefficient is not an appropriate statistic for measuring their association. Is the number of games won by a basketball team correlated with the average number of points scored per game? A scatterplot indicates that there is a linear relationship. Analyzing data from the 1994–1995 NBA season yields that Pearson’s correlation coefficient (0.581) is significant at the 0.01 level. You might suspect that the more games won per season, the fewer points the opponents scored. These variables are negatively correlated (–0.401), and the correlation is significant at the 0.05 level.
Analyze- > Compare Means -> Partial The Partial Correlations procedure computes partial correlation coefficients that describe the linear relationship between two variables while controlling for the effects of one or more additional variables. Correlations are measures of linear association. Two variables can be perfectly related, but if the relationship is not linear, a correlation coefficient is not an appropriate statistic for measuring their association. Is there a relationship between healthcare funding and disease rates? Although you might expect any such relationship to be a negative one, a study reports a significant positive correlation: as healthcare funding increases, disease rates appear to increase. Controlling for the rate of visits to healthcare providers, however, virtually eliminates the observed positive correlation. Healthcare funding and disease rates only appear to be positively related because more people have access to healthcare when funding increases, which leads to more reported diseases by doctors and hospitals.
Analyze- > Compare Means -> Distances This procedure calculates any of a wide variety of statistics measuring either similarities or dissimilarities (distances), either between pairs of variables or between pairs of cases. These similarity or distance measures can then be used with other procedures, such as factor analysis, cluster analysis, or multidimensional scaling, to help analyze complex datasets. Is it possible to measure similarities between pairs of automobiles based on certain characteristics, such as engine size, MPG, and horsepower? By computing similarities between autos, you can gain a sense of which autos are similar to each other and which are different from each other. For a more formal analysis, you might consider applying a hierarchical cluster analysis or multidimensional scaling to the similarities to explore the underlying structure.

Generalized Linear Models

Analyze- > Generalized Linear Models -> Generalized Linear Models The generalized linear model expands the general linear model so that the dependent variable is linearly related to the factors and covariates via a specified link function. Moreover, the model allows for the dependent variable to have a non-normal distribution. It covers widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, loglinear models for count data, complementary log-log models for interval-censored survival data, plus many other statistical models through its very general model formulation. A shipping company can use generalized linear models to fit a Poisson regression to damage counts for several types of ships constructed in different time periods, and the resulting model can help determine which ship types are most prone to damage.

 

A car insurance company can use generalized linear models to fit a gamma regression to damage claims for cars, and the resulting model can help determine the factors that contribute the most to claim size.

 

Medical researchers can use generalized linear models to fit a complementary log-log regression to interval-censored survival data to predict the time to recurrence for a medical condition.

 

Regression

Analyze -> Regression -> Linear Linear Regression estimates the coefficients of the linear equation, involving one or more independent variables, that best predict the value of the dependent variable. For example, you can try to predict a salesperson’s total yearly sales (the dependent variable) from independent variables such as age, education, and years of experience. Is the number of games won by a basketball team in a season related to the average number of points the team scores per game? A scatterplot indicates that these variables are linearly related. The number of games won and the average number of points scored by the opponent are also linearly related. These variables have a negative relationship. As the number of games won increases, the average number of points scored by the opponent decreases. With linear regression, you can model the relationship of these variables. A good model can be used to predict how many games teams will win.
Analyze -> Regression -> Binary Logistics Logistic regression is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model. Logistic regression is applicable to a broader range of research situations than discriminant analysis. What lifestyle characteristics are risk factors for coronary heart disease (CHD)? Given a sample of patients measured on smoking status, diet, exercise, alcohol use, and CHD status, you could build a model using the four lifestyle variables to predict the presence or absence of CHD in a sample of patients. The model can then be used to derive estimates of the odds ratios for each factor to tell you, for example, how much more likely smokers are to develop CHD than nonsmokers.
Analyze -> Regression -> Multinomial Logistic Regression Multinomial Logistic Regression is useful for situations in which you want to be able to classify subjects based on values of a set of predictor variables. This type of regression is similar to logistic regression, but it is more general because the dependent variable is not restricted to two categories. In order to market films more effectively, movie studios want to predict what type of film a moviegoer is likely to see. By performing a Multinomial Logistic Regression, the studio can determine the strength of influence a person’s age, gender, and dating status has upon the type of film they prefer. The studio can then slant the advertising campaign of a particular movie toward a group of people likely to go see it.
Analyze -> Regression -> Ordinal Regression Ordinal Regression allows you to model the dependence of a polytomous ordinal response on a set of predictors, which can be factors or covariates. The design of Ordinal Regression is based on the methodology of McCullagh (1980, 1998), and the procedure is referred to as PLUM in the syntax. Ordinal Regression could be used to study patient reaction to drug dosage. The possible reactions may be classified as none, mild, moderate, or severe. The difference between a mild and moderate reaction is difficult or impossible to quantify and is based on perception. Moreover, the difference between a mild and moderate response may be greater or less than the difference between a moderate and severe response.
Analyze -> Regression -> Probit This procedure measures the relationship between the strength of a stimulus and the proportion of cases exhibiting a certain response to the stimulus. It is useful for situations where you have a dichotomous output that is thought to be influenced or caused by levels of some independent variable(s) and is particularly well suited to experimental data. This procedure will allow you to estimate the strength of a stimulus required to induce a certain proportion of responses, such as the median effective dose. How effective is a new pesticide at killing ants, and what is an appropriate concentration to use? You might perform an experiment in which you expose samples of ants to different concentrations of the pesticide and then record the number of ants killed and the number of ants exposed. Applying probit analysis to these data, you can determine the strength of the relationship between concentration and killing, and you can determine what the appropriate concentration of pesticide would be if you wanted to be sure to kill, say, 95% of exposed ants.

Classify

Analyze -> Classify -> K-Means Cluster Analysis This procedure attempts to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases. However, the algorithm requires you to specify the number of clusters. You can specify initial cluster centers if you know this information. You can select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only. You can save cluster membership, distance information, and final cluster centers. Optionally, you can specify a variable whose values are used to label casewise output. You can also request analysis of variance F statistics. While these statistics are opportunistic (the procedure tries to form groups that do differ), the relative size of the statistics provides information about each variable’s contribution to the separation of the groups. What are some identifiable groups of television shows that attract similar audiences within each group? With k-means cluster analysis, you could cluster television shows (cases) into k homogeneous groups based on viewer characteristics. This process can be used to identify segments for marketing. Or you can cluster cities (cases) into homogeneous groups so that comparable cities can be selected to test various marketing strategies.
Analyze -> Classify -> Hierarchical Cluster Analysis This procedure attempts to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case (or variable) in a separate cluster and combines clusters until only one is left. You can analyze raw variables, or you can choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure. Statistics are displayed at each stage to help you select the best solution. Are there identifiable groups of television shows that attract similar audiences within each group? With hierarchical cluster analysis, you could cluster television shows (cases) into homogeneous groups based on viewer characteristics. This can be used to identify segments for marketing. Or you can cluster cities (cases) into homogeneous groups so that comparable cities can be selected to test various marketing strategies.
Analyze -> Classify -> Discriminant Discriminant analysis builds a predictive model for group membership. The model is composed of a discriminant function (or, for more than two groups, a set of discriminant functions) based on linear combinations of the predictor variables that provide the best discrimination between the groups. The functions are generated from a sample of cases for which group membership is known; the functions can then be applied to new cases that have measurements for the predictor variables but have unknown group membership.

Note: The grouping variable can have more than two values. The codes for the grouping variable must be integers, however, and you need to specify their minimum and maximum values. Cases with values outside of these bounds are excluded from the analysis.

 

On average, people in temperate zone countries consume more calories per day than people in the tropics, and a greater proportion of the people in the temperate zones are city dwellers. A researcher wants to combine this information into a function to determine how well an individual can discriminate between the two groups of countries. The researcher thinks that population size and economic information may also be important. Discriminant analysis allows you to estimate coefficients of the linear discriminant function, which looks like the right side of a multiple linear regression equation. That is, using coefficients a, b, c, and d, the function is:

D = a * climate + b * urban + c * population + d * gross domestic product per capita

 

If these variables are useful for discriminating between the two climate zones, the values of D will differ for the temperate and tropic countries. If you use a stepwise variable selection method, you may find that you do not need to include all four variables in the function.

 

Dimension Reduction

Analyze -> Dimension Reduction -> Factor Analysis Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance that is observed in a much larger number of manifest variables. Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis (for example, to identify collinearity prior to performing a linear regression analysis).

The factor analysis procedure offers a high degree of flexibility:

• Seven methods of factor extraction are available.

• Five methods of rotation are available, including direct oblimin and promax for nonorthogonal rotations.

• Three methods of computing factor scores are available, and scores can be saved as variables for further analysis.

 

What underlying attitudes lead people to respond to the questions on a political survey as they do? Examining the correlations among the survey items reveals that there is significant overlap among various subgroups of items–questions about taxes tend to correlate with each other, questions about military issues correlate with each other, and so on. With factor analysis, you can investigate the number of underlying factors and, in many cases, identify what the factors represent conceptually. Additionally, you can compute factor scores for each respondent, which can then be used in subsequent analyses. For example, you might build a logistic regression model to predict voting behavior based on factor scores.
Analyze -> Dimension Reduction -> Correspondence Analysis One of the goals of correspondence analysis is to describe the relationships between two nominal variables in a correspondence table in a low-dimensional space, while simultaneously describing the relationships between the categories for each variable. For each variable, the distances between category points in a plot reflect the relationships between the categories with similar categories plotted close to each other. Projecting points for one variable on the vector from the origin to a category point for the other variable describe the relationship between the variables.

An analysis of contingency tables often includes examining row and column profiles and testing for independence via the chi-square statistic. However, the number of profiles can be quite large, and the chi-square test does not reveal the dependence structure. The Crosstabs procedure offers several measures of association and tests of association but cannot graphically represent any relationships between the variables.

Factor analysis is a standard technique for describing relationships between variables in a low-dimensional space. However, factor analysis requires interval data, and the number of observations should be five times the number of variables. Correspondence analysis, on the other hand, assumes nominal variables and can describe the relationships between categories of each variable, as well as the relationship between the variables. In addition, correspondence analysis can be used to analyze any table of positive correspondence measures.

 

Correspondence analysis could be used to graphically display the relationship between staff category and smoking habits. You might find that with regard to smoking, junior managers differ from secretaries, but secretaries do not differ from senior managers. You might also find that heavy smoking is associated with junior managers, whereas light smoking is associated with secretaries.

Reliability Analysis

Analysis -> Scale -> Reliability Analysis Reliability analysis allows you to study the properties of measurement scales and the items that compose the scales. The Reliability Analysis procedure calculates a number of commonly used measures of scale reliability and also provides information about the relationships between individual items in the scale. Intraclass correlation coefficients can be used to compute inter-rater reliability estimates. Does my questionnaire measure customer satisfaction in a useful way? Using reliability analysis, you can determine the extent to which the items in your questionnaire are related to each other, you can get an overall index of the repeatability or internal consistency of the scale as a whole, and you can identify problem items that should be excluded from the scale.

 

Experiences and digitalization – where are we going?

It’s all about experiences these days, isn’t it? They are constantly discussed in the field of tourism, and with other industries as well, but do we actually know what it is about the idea of experience that in the end intrigues the customer enough to make a purchase decision?

No, we cannot know that. Why? Because experience is a subjective, individual and very unique concept. No one experiences a product or service the same as their peers. Someone might not even feel as though they have received any kind experience from a service or product which might have been completely transformational for someone else. So, how do we market experiences, if we cannot guarantee that there is even going to be an experience to have? Can we enhance the experiences with tools provided by digitalization?

We must know our customer segment and what they are searching in their travels in order to understand how they might see the meaning of experience. Finding the customers ready to receive those experiences and even pay for them is not probably going to be difficult as marketing online develops, and it gets easier to attract bigger masses or find the niche market inside those masses that want your products.

How will digitalization change experience design?

In the future, digitalization and online marketing will be the key element in marketing experiences. As the world of digitalization moves forward, we can expand our experiences and how we see them. It opens totally new doors for marketing; A customer puts a pair of VR glasses over their eyes. They jump through a series of videos; snow, reindeer petting, Santa Claus, northern lights. The pretty picture formats in their head. I have to see that for myself. The thought of perfect winter wonderland has been set in their mind. The spark is there. All you need is the product to sell.

Snow dusting, formation of experience
Will there be time when experiences, like the feeling of snow and seeing the northern lights, can be designed fully online? Photo: Pulkkinen 2015

The question then becomes; how much we can tease the potential customers? Where is that line of wanting the hands-on experience, instead of watching northern lights lying on your own bed with the VR glasses on, enjoying the comfort and safety of your own home? What added value does the customer get from coming to Finland and going to freeze in the middle of the lake to watch northern lights? And how we can keep that experience authentic to the customer?

It’s about evolving. Designing. You need to find the link between the need to evolve with digitalization and the benefit for your company. What can you do in order to enhance the hoped customer experience? It’s about designing, prototyping and trying. Co-creation, another big word. Co-creation will most likely get on a different level with digitalization in the future, as information sharing and possibilities to do online get wider and wider. We are in a state of constant development.

The question remains. Digitalization, opportunity or threat to experiences in the tourism field?

Maybe both? The key is to find what is the best possible practice for you.

3 easy ways to make online customer experience better in tourism

As technology develops it is necessary for tourism businesses as well as businesses, in general, to think about how they can improve online customer experience. There is a lot of talk about if technology will replace human interaction, but actually in business purposes technology can help make customers happier. Although we have to remember that to do so, it has to be done correctly and enhance the customer experience rather than make things more complicated.

Our class was brainstorming to find out how technology can delight customers in tourism businesses. We discovered a lot of different ways, but there were three that stood out the most for all of us: easily accessible information, photos & videos, and helpful marketing.

Easily accessible information

When planning a holiday or booking activities, one of the biggest things is that the information is comprehensive and easily found. Tourism companies should focus more on the content of their website as it is the main source of information for customers. This means that the navigation should be clear and there should be information available about everything the company is offering. Especially for tourism companies, online booking is one thing that will make customers happier as well as the possibility to give feedback online. Easier the information search process is the happier the customer gets.

Photos & videos

We all know the saying “a picture tells more than a hundred words”. This should be kept in mind when marketing your products or services. Photos and videos can increase the trust as well as the satisfaction of customers when they match with reality. As an example, if a hotel has a gym, but they have no pictures of it, the hotel might seem a bit shady for the customers. But then again, you should not use pictures or videos that are much better than the reality, as the reality will then disappoint the customers. It’s also good to remember that high-quality photos and videos are also quite nice to watch, and they easily caught the eye in social media.

Helpful marketing

No one likes when a business is by force trying to sell their products or services, at least not Finnish people. Tourism companies should use more helpful marketing, which means that you introduce the strengths and benefits of our products or services rather than use a phrase such as “buy now”. In helpful marketing, you create content that is helpful for customers and that creates value for your customers. In the end, it will also lead to better sales. Make the customer feel special and be interactive on social media channels.

Focus on online customer experience!

As said in the beginning, there can be multiple ways to make customers happier by utilizing technology. Get started with the three ways introduced here, but remember that you can always develop ways that suit your company and customers better. In the end it all comes down to understanding the online customer experience!

How to successfully master website design for your tourism business?

An effective website is important for your tourism business as it is one of the first touchpoints in online marketing. We developed a framework of the most important core factors in website design. Instead of digging deeper into each of them, we look at the whole picture.

Website Marketing_Different Devices

STRUCTURE AND DESIGN

First, let’s start with the basics. Design the website in an easy, clear and simple structure. Be visual! Hence, make customers remember your website. Furthermore, include a navigation panel. You want to make sure that your customers find exactly what they’re looking. And pay attention: less is sometimes more. An over-designed website is not only daunting for the visitor but also impacts the page speed. Trust me, the goal is to attract more customers, not to lose them.

ACCESSIBILITY

Ensure quick-loading pages by choosing the right technology, smaller compressed images and a simple website design. Pay attention to the general accessibility on different platforms, devices and browsers. Don’t despair! You can even make a test run and check the page speed on different devices before you publish it.

ENGAGING CONTENT

There is no need for an attractive and clear structured website if your visitors are getting bored to death. Hence, create engaging content by integrating your personal story and stating your missions, vision and goals clearly. Show your customers that you care and include their testimonials and reviews as well as your values by adding sustainability certificates or Corporate Social Responsibility awards.

Picture_Metaphor for Storytelling

STORYTELLING

Most importantly, think about your customers and their values and implement them in your story. Don’t forget to add a beginning and an end and build up excitement. Captivate the customers with a powerful message.

Include interactive elements like videos or photos of your products or links to your social media channels. Then, think a step further and use chatbots to guarantee a round-the-clock personal customer care. Furthermore, retarget customers with discounts or feedback sections.

All in all, the possibilities online are endless. Just remember: taking your customers’ needs and your own values into account is the goal to succeed in digital marketing. Use Website Analytics and Search Engine Optimisation and dedicate a website manager to ensure constant updates, trouble-shooting and optimisation.

Follow this framework and you’re all set to go! What are you waiting for?

Nostalgia in marketing – a great way to drown your business

It is pretty easy to say that marketing a business today is made pretty affordable and easy – if you are not stuck in the nostalgia in marketing and trust that a basic “block ad” on a magazine is an effective way on advertising.

Events are my cup of tea. As an event manager, you are DEPENDENT on that the products are visible on Google and social media channels. The homepage should be active with links to other pages for Google to find it interesting, Adwords should rise up your event every time you are searched and ads can be targeted to customers who have visited the site. With the basic effort, you can do all this by yourself and can gain a lot of new customers. Without these things, you are same as dead to your customers.

Why?

Most of the customers, who’s from you are interested (read: have money and interest towards your hip-product), especially in technology orientated country like Finland, use smartphones – or at least search your business via computer. So why bother on spending lots of money on ads on e.g. newspapers? To compare online marketing to classic marketing on print – here’s just a plain and simplified example:

Marketing on local newspaper:  One marketing ad on leading local newspaper (outside southern Fi) Size approx. a6 format. Shown during one specific day. Cost approx. 800 €

  • You’ll need  (or at least it is highly preferred) to buy visuals from a professional (60-70 e / h)
  • The ad is shown one time on today’s magazine with other ads on the third last page.
  • Circulation of the magazine approx. 100 000 people in one province with broad age cohort.
  • No reliable ways to measuring the effects of the ad.

Marketing on Facebook/IG: for the same money (900 €) possibilities:

  • Do ads by yourself (of course the expertise and counseling of a professional- like yours truly – is always recommended) 😉
  • Choose the most relevant target groups from e.g. Helsinki and your hometown with specified age cohort and interests to visit the homepage or buy the tickets straight away.
  • Set the ads to be visible e.g. for week or two.
  • See how many times your ad is clicked, measure and optimize the ad also afterwards.
  • Gain more followers to your site, where you can advertise the event more specifically and share the love with your likers.
  • Add FB Pixel and google tracking to your homepage andthe engage same people who may have visited your site but not yet bought the tickets by encouraging them to buy tickets afterwards via cookies set on their devices.
So. Which one would you choose as an advertiser?

Are printed ads about to die? No. There is a place for it as well. E.g. in the form of advertorials in the specific periodicals or example big posters are really good way of getting attention – they have more possibility to reach your target groups. Books are read still, and I for one will always prefer to read from the paper than from the tablet or such. But it is hard to see a long-headed future for classic “box-ads” in the newspaper or such as an efficient way of reaching your customers now or especially in the future. Do not let nostalgia in marketing make decisions for you!

Do Not Read This!

Content Marketing?

Yep, I thought you wouldn’t listen. Luckily there is no harm in reading this, quite the opposite. I just wanted to prove a point: In content marketing, you are competing of customers enjoying an overflow of information and suffering lack of time. Therefore, your content marketing needs to be intriguing and catch the attention. Like hopefully this headline did. So, in case you are interested in content marketing, you should stick around and read rest of the post!

Let’s start with discussing the term. It can be mixed up with social media marketing. Creating an ad in Facebook is still not content marketing, just marketing happening in social media. The idea of content marketing is to create different types of content, like blogs, video material and online discussion with customers. The content is shareable and creates interest towards your business, increases your appearance in search engine results and should affect your sales.

How to start

So, let us say that you just established a hotel in Lapland. Should you now open accounts to every other social media outlet and fill them with room pictures and other posts? No, first comes the content marketing plan. For that you need to know your potential customers and their preferences. With bench-marking rival companies, you can learn what content they share and what social medias they use. Referencing the competition is a good start, but also personal goals need to be established. What does your company achieve with all the content you share? Who sees it and what do they do with it? When you can answer these questions, you can proceed with the plan.

Some No No’s

Next, lets dodge some common landmines of content marketing. First, the content can’t be all about the company. Do not always sell something, the customers are not always buying something. The key idea is to be on mind of the customers and when they want to make a purchase that you can provide, they will remember you.

Television was a one-way media, but social media is not. By forgetting this, you miss out on the potential of content marketing and might even do damage. Be attentive to the customers, answer the complaints and discuss the questions.

Do not do too much. If your customers discuss your services in Facebook and like your pictures in Instagram, but Twitter is not working, concentrate on the former. That way your company can be consistent and reliable online, which creates trust also in general.

Do not forget to track the impact of your content marketing: It is essential for you to know how it works and what should be developed or altered.

Entertain, Inspire, Educate and Convince

Content should be useful to both existing and potential customers. In general, you should follow these four guidelines:

  1. Entertain the customer with top quality content to spend time in your media and get them to know you.
  2. Inspire them and help them to create new ideas and new dreams.
  3. Educate the customer on the issues related to your business and create a feeling of trust.
  4. Finally, convince the customer to use your services to retain the best possible solution to his or her problem.

Know your customers to know the preferred social medias and content. Track the impact and you know what to develop. By being consistent and attentive, you are reliable and on people’s mind.

Self-employed Business Owner: Why Learning Basics of Digital Marketing Saves you Money while Growing your Business?                                                                                                 

Are you preparing cottage rooms for next guests? Driving dog sledge through low-lying arctic hills? Preparing dinner for a group of visitors? If you’d take less than 10 minutes of your time to read through this blog post, I can promise you’ll be even busier after few months’ time.

So, do you have time to talk about getting better return to your hard-earned money that you put to marketing? Especially if the marketing and digital marketing frustrates you at the moment. I know – I’ve been there. And now I’m writing this blog post to You.

Most of the small tourism companies do have web pages and they are in the Facebook, but it doesn’t automatically mean that your customers will find you. You can be like the wall paper that is in the store but cannot be found. Or even like a pretty nice looking wall paper put on display but still don’t get customers’ attention. In order to break through from “the lost wall paper corner of internet” and get the awareness you deserve from potential customers, I recommend to consider the following three things:

  • What are the key words that your webpage is optimised for?

    Key words are crucial “tags” to help search engines like Google to find your web page when your potential customers are searching for services that also your company offers. Key words help your web page appear in the first page of Google search results. Also having links to other relevant web pages serve the same purpose.

  • Content is the King! 

    …in all channels. I understand that you don’t have time to be in Facebook or in other channels every day and figure out what you should post or write. You have real customers to serve. To make this easier to yourself make a list or even a calendar. Just listing what you’ll update and when keeping it simple and realistic. You don’t have to go from no activity to ten activities during a week at once. Good content frequently is ok. The content can be e.g. photos, happy customers (with their permission), positive customer feedbacks (testimonials) or even something cleaver about the weather. And of course, customers want to get to know you. Tell your story – in the extent that you feel comfortable with.

    While you are posting photos in Facebook you can do that as well in Instagram. You can open an Instagram page for your business here: https://www.instagram.com/accounts/login/. In Instagram use those hashtags (#) with the words you want your business to be connected with.

By to way, all those likes, shares and comments in your social media pages are worth money. This is not online marketing basics anymore, but if you are interested in deepen your knowledge you can listen more about this here: https://www.youtube.com/watch?v=Im26jZT-eQw. I promise, this is useful.

  • Get to know the magical place of Google Analytics

Measuring outcomes of our actions is as relevant in digital marketing as it is in other areas of the business. Addition to the fact that it shows you how well did you succeed, that you don’t end up buying possibly too expensive marketing actions which don’t deliver what you hoped them to. Useful tool to follow up your online marketing actions is Google Analytics. See easy first steps to take to get started here: https://www.youtube.com/watch?v=lZf3YYkIg8w.

Optimising your web page so that customers find it is the first step. But as a second step you can make advertisements online to boost your visibility. Just remember to have proper web page before you advertise it: updated, relevant information to customers and possibility to buy your services, if the online selling is relevant for your business model. There are free tools to test the quality of your web page like this one: https://www.seoptimer.com/. They also give recommendations to improve your site.

 

If you got at least a little bit interested in, please Google your business – not with the name of the business, but with some other words that you’d use as a customer looking for the kind of services your business offers. If you find your business web page from the first page and your competitors are below you in Google search results, you have done something right. Congratulations! If you, on the other hand, find yourself from the second or the third page in Google search results, you’ve been able to hide your business to the place where no one looks – to the lost wall paper corner of internet. But don’t worry, you can break out from there by getting started with the steps described in this article. If you find this difficult or you just don’t have time for it, you can buy this as a service. And now you have better understanding what you are paying for.

How information technology can help in customer relationship

The customer comes first.  Classy saying, right? Surely that guideline, or perhaps a cliché, creates some kind of thoughts in your head. Do you consider it important?

Customer relationship is also such a classic term in the business world. Well, that for sure is essential! However, which elements in customer relationship matter the most for DMO’s and tourism businesses, and how could information technology could help?

Master level business students in Information Technology in Tourism -course figured it out, via rather a successful brainstorming. Firstly, all the possible elements that belong to customer relationship were considered. Secondly, the most important ones of them were marked. So, let’s start digging deeper into the world of the customer relationship, and how to combine it with information technology.

Five key groups were created out of all: interaction, customer engagement, networks, feedback, and personality. Each of these included numerous of subgroups. There is reallyy no point introducing all the subjects that came across. Instead, let’s have a closer look to the top 5 topics that were marked the most important of all and consider how information technology could help in tourism business generally.

Trust

Simply put: manage your customers’ expectations and do what you promise. Take care of your customers, for example by protecting their data. Show your reviews and customer testimonials, be open and transparent.

Co-creation

Listen to your customers, and also ask them. After doing by their suggestions, measure how you’ve been doing things to basically see if you’ve got it right. There are numerous ways to measure your online success, make sure you use at least one of them.

Customer Relationship Management

Naturally, managing your customer relationships generally is important. Your ideal CRM should form in simplicity, price, and relevance best suitable for your business. There are many software and electronic systems to help you with that. Why not give them a try?

Personality

In every step of your customer relationship, make sure that the customers recognize it’s you they are working with. If you have a personal style to do things, keep it and embrace it! That is easy to do in the digital world, just create a unique look to suit your company and publish similar looking style in each content.

24/7 availability

It might need resources to have 24/7 availability but do your best. Being a fast answerer creates trust. It also allows you to do co-creation with your customers. What comes around goes around. If a customer is so interested in what you are offering that they are contacting you, use this opportunity well!

Therefore, next time you are considering how well customer relationships are taken care of in your tourism business, think about the topics discussed and how well they are being managed. And trust it, you’ve got it.

 

 

SMEs should use web analytics for competitive advantage

A first semester studying Tourism Marketing and Management at UEF showed that most of the tourism companies in Finland are small-to-medium enterprises (SMEs).  These companies are trying to market their offerings to consumers, by directing them to the company’s webpage to increase traffic, and hopefully future profits. In many cases, this is where the focus on the customer journey stops. Nowadays, it’s not enough for the SMEs to direct traffic to their webpage. The companies have to know what potential customers are doing there.

For various reasons, many SMEs have not considered web analytics as something that would be beneficial for future success. Everyone is online nowadays and many make most of their purchase decisions using at least some kind of online materials. For SMEs it is especially important to think long and hard, where and how to spend the marketing budget. Without the use of web analytics, it is next to impossible for them to properly analyse the results of their marketing campaigns.

The traditional view of web analytics is that it is only for giant companies, SMEs should steer away from this kind of thinking. The presumption is entirely misleading. All companies can and should use web analytics tools! Without web analytics tools, it is difficult to see the results of marketing campaigns. Analytics give SMEs insight into what they’re doing right and what could be improved.

There are many different web analytics tool out there. Understandably, it might be confusing for entrepreneurs to get started. For tracking traffic and conversions companies could start using Google Analytics. From Google Analytics, SMEs can get a huge amount of data. This tool is highly beneficial for tracking the success of various marketing campaigns. Web analytics can also be used to track the performance in social media. Many customers nowadays use social media to form an overview of the company that they are buying from. Many social media platforms offer the tools to track the company’s performance. There are no reasons for SMEs not to use these tools.

The use of web analytics tools helps SMEs to understand their customers and this way develop competitive advantage! You can do digital marketing without analytics, but the only way to get the best bang for your buck is to connect your digital marketing efforts to digital analytics.

Tourism studies at University of Eastern Finland: application criteria and scholarships in 2018

How to apply to tourism studies at the master’s degree programme in Tourism Marketing and Management? We have now published application criteria and process for the 2019 intake!

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