I often find that business owners and individuals have a hard time identifying relevant keywords for HootSuite searches and for...Read More
This article is also available as a podcast Considering a few different versions to your landing page? Not sure about the color...Read More
It took some time, but finally my book is out! You can download it from Amazon: Getting a Job in Marketing Would love to hear...Read More
I found this book in the clearance section of a book store not long ago. I enjoyed it and found it was insightful. It’s mainly sales oriented, but anyone pitching their startup can benefit from seeing themselves as sales people. If you don’t feel like reading the whole thing here are the main points:
- Give the customers a little extra-more than they expect
- Prepare for sales calls. Don’t just “call.”
- The most important question when you call to set a meeting- do you have your appointment schedule handy?
- When you meet at cafe/restaurant, choose sit carefully so prospect doesn’t get distracted. You will be facing street or marina, they will be facing you and the wall
- Coffee is bad! Do not drink coffee on the way to an important meeting- a spill can kill! Also keep your stuff away from someone else’s coffee. When arriving at the meeting, say no to a drink. It is a time waster
- Same for food. When you go for lunch, don’t start asking the waiter questions. Avoid eating food that you have to mess with or will stick to your teeth
- When you are half way through doing your job for the customer, start preparing for the next job. Find out what else you can do for the same customer.
- Don’t create enemies. Treat everyone with courtesy, they can all be clients.
- Don’t put a pen in your pocket! It can kill your shirt
- Ask the customer something like: “Will you look at the facts and decide for yourself?” which triggers an automatic “yes”.
- “Always taste the wine before you give it for testing”- before you give any demo, speech, give a product to someone- make sure everything works perfectly.
- Turn off phone before meeting customers. A call is a distraction. Never answer calls while you are with a customer.
- When the customer calls, you are never “in a meeting”. You are with another customer and you will get back to them. You are never on “vacation”- you had to travel abroad. You are never sick- you left the office early to meet with customers.
- Get some commitment before committing to come and do a demo: “if the demonstration is successful, is there anything else prohibiting you from going ahead?”
- Don’t chit chat when you get to a meeting with the customer. Both yours and her time is precious. Don’t start talking about her kids. Get to the point. After you have made the sale, near the elevator, you can “break the ice”.
- Create a point system that gives different points to: getting referrals, making a sales call, meeting a client – and make sure you get at least 4 points a day.
- “Why don’t you give it a try”? implies that you can change your mind, and gives the customer some comfort.
- Prepare ahead your voicemail msg, so the customer will want to hear more from you. Be brief. Announce in the beginning that it is going to be short. tell them that you will call back in case they can’t reach you.
- Park in the back and organize yourself where the customer can’t see you. You don’t want to be straightening your skirt when you meet the customer. You must look 100% professional.
- Dress one level above the customers you are going to meet.
- Meet for breakfast, before the day starts and there are things can go wrong and cause cancelations
- Killer questions: “what question should I be asking and I am not asking?”, “have I covered everything?”
World Pride June 2014, Toronto
Since today being well-connected in the world of social media represents power to many, a large industry of fake friends and followers emerged, and is estimated to be worth about $340 million a year. The online popularity contest is going on in full power and it has no mercy.
Today I am going to share with you how these fake followings work, how fake followers can be detected, and we will look at some celebrity profiles on twitter and check how many fake followers they have.
How can a person get 10,000 new followers in a day?
It all starts with the software. If we look at twitter, for example, the software is often referred to as a “twitter bot.” Someone develops software that can create hundreds or thousands of new twitter profiles per hour. The only limitations will be the speed of your computer and the speed of your internet. These software packages also use something known as “alternating IP addresses,” so the Twitter server can’t identify that all the new profiles are being submitted from the same location.
The software generates a twitter login for each one of the new users it creates. Sometimes the method is taking bits of information from different real profiles and mixing them together to create a new (fake) profile. Software differs in quality – some software will put up a picture as well and write some tweets in order to make the profiles seem more real. The price people pay for the service varies accordingly. After the profiles are made, the software owner gives the fake users the command to start following a specific account.
Individuals who buy or obtain a pirated copy of the software then proceed to offer their services to others in online marketplaces. One such a marketplace is fiverr.com. On fiverr you can buy almost anything for five dollars; people will design a website for you, write a press release, make a business plan, record a video of themselves singing happy birthday to your girlfriend, and more. One of their most popular categories involves selling Facebook friends and likes and twitter followers. One of their users, for example, sells 6000 Twitter followers for five dollars. After such a service is ordered, within a few hours the person making the order will have an additional 6000 followers to their twitter account.
Who buys fake Twitter followers?
It could be anyone, but is mainly attributed to those who are deep into the popularity contest and have to show a larger number of followers in comparison to their colleagues or competitors: celebrities, companies, and politicians. However, since it costs only five dollars to add a large numbers of followers, many anonymous individuals engage in the practice as well.
A recently introduced tool, status people, claims to be able to spot fake users following someone’s profile. So let’s look, for example, at the Twitter profile of Britney Spears.
How can fake followers be spotted?
Even though status people does not fully expose its algorithm, their website states:
We take a sample of your follower data. Up to 1,000 records depending on how ‘popular’ you are and assess them against a number of simple spam criteria. On a very basic level spam accounts tend to have few or no followers and few or no tweets. But in contrast they tend to follow a lot of other accounts.
Fake followers can be identified in the following ways:
Minimum details in the profile – while real people like customizing their profile and typically add some details like a bio and custom URL, fake profile bots work on large quantities thus will input the minimum information necessary to open a profile, in order to open the most number of profiles in a certain timeframe.
Low follower / followed ratio- a fake profile will be following many more people than are following it, since the software makes it follow a lot of people, but no one has an interest in following a fake profile.
No profile picture- using someone else’s photo in one’s profile is highly unethical and can result in legal problems, thus many times fake profiles will not have pictures.
Access diversity – it is common for a real profile to be used on different devices – for example someone may use their mobile phone to upload a photo to twitter, and look for their friends twits on the computer at work. A fake profile will normally have one access point – that PC from which the robot works.
Tweet count – it is commonly believed that a fake profile will have very few tweets or none at all.
However, spotting fake profiles remains a challenge, since everything I described can apply to a real user as well, especially a real non-active user. If it were easy to spot fake profiles, Twitter could simply get rid of them systematically. In addition, some of the more advanced tweet robots will add more information to the profile, post occasional random sentences that they pick up from news websites, for example, and trick the Twitter server into thinking the profile is being accessed from different devices. The end result is that some high-end fake profiles may seem more real than many real profiles. The more advanced the software is, the more the “owners” charge to customers for the fake followers.
Let’s take a look at a few celebrities and their fake follower count, as suggested by the status people software:
However, the fact that someone has fake followers doesn’t necessarily mean that they bought them. The best way to point out fake twitter followers and knowing that they were ordered from robots is by looking at trends. There is a tool called Twitter Counter, that looks at the trends in number of followers and number of tweets. This tool allows us to see spikes in twitter followings that can’t be naturally generated. When an attempt to boost popularity by using fake followers is spotted, it can be harmful and embarrassing. An example can be seen in Mitt Romney’s twitter scandal – last July (2012) his account generated 116,000 followers in a single day.
What makes celebrities have so much fake following?
It seems that many fake profiles follow celebrities in order to appear more legit (operating under the assumption that it is common for a person to follow celebrities). This effect was demonstrated by Jason Ding of Barracuda Labs who purposely bought fake followers and later analyzed their profiles. He found out that these profiles tend to follow more celebrities than real profiles. Then again, this could also be explained if we claimed that celebrities buy more fake followers.
Also, it is possible that many people open an account only to follow a specific celebrity and have no other activity – thus the real profile may appear to be fake. This is also true for someone who follows celebrities until one day he or she gets bored and doesn’t log into twitter anymore. This person’s profile may appear to be either inactive or fake.
Big Brands Have To Be Careful When Listening to the Online Conversation.
Amanda says in a forum: “I have an iPhone 4. It sucks.” Her post, along with many others, is gathered by “online sentiment” robots trying to make sense out of them and give the clients (the companies and brands) some insights, like “30% of the online talk about the iPhone 4 is negative.” The question is, does this make sense? What real information can companies get out of statements such as Amanda’s? In our case, it is possible that Amanda is dissatisfied with Apple’s iPhone, but it is also possible that she is waiting to get an iPhone 5. Meantime, she has to “endure” her iPhone 4, and it sucks.
That companies want to understand what their customers think is old news. Companies have been conducting surveys among their customers and among the general public for years now, trying to get an insight into what people think about the company and its products. However, these surveys were not proven to be a real predictor of customers’ actions. For example, a customer may complain in the survey about an airline and say that the service is horrible and he’s never going to fly with it again, yet remain loyal since it’s the only company that has a direct flight on the routes he normally travels.
Extracting Brand Sentiments from Online Conversations – Challenge #1
So, the next step was to eavesdrop on consumers when they are having conversations online, and are not necessarily trying to communicate with the brand. The web is full of conversations, many of them about specific brand – on Facebook, Twitter, blogs, forums, reviews, and more. When a brand is very small, this “eavesdropping” can be done manually – a person or a team inside a company can try to constantly monitor what people are saying about the brand by simply searching for the brand name on search engines and on social websites. When the brand is larger, however, the online conversation can consist of hundreds or thousands of mentions daily – making it impossible to track them manually.
This quest to harvest the sentiment towards the brand brought on the emergence of services that claim to be able to collect large amounts of “online conversation” data about a brand and algorithmically analyze it. These services then output a number representing the brand sentiment – for example, by checking the percentages of negative and positive mentions out of all mentions in total – and giving the client reports such as: “30% positive, 20% negative and 50% neutral.”
This analysis is a challenge, since unlike human beings, who can pick up sentiment easily even from not-so-obvious sentences, algorithms have to follow a predetermined set of rules and look for specific sentiment-oriented words that are not always present in the conversation. Check, for example, this sentence: “why would Apple have this application in the iTunes Store anyway??” Put in the right context, many humans will accurately detect this to be a negative comment about a certain application. To an algorithm, however, this can appear to be a neutral statement, owing to its lack of overtly negative words.
Extracting Brand Sentiments from Online Conversations – Challenge #2
In an MSI insights article called “measuring online brand sentiment,” Julia Hannah writes about the current hot issue of trying to extract meaning out of large quantities of word-of-mouth. The paper describes a large-scale academic study by David Schweidel and Wendy Moe examining word-of-mouth sentiment across different online venues. Schweidel and Moe do not focus on the problem of an algorithm’s limited ability to understand context, they are interested instead in inherent differences in sentiment that exist across venues, products, and brand attributes.
For example, conversation venues that are more ‘one to many’ in their communication style (such as Twitter and blogs) tend to have more positive posts than venues that are more ‘many to many’ communication style (such as forums). They suggest that this “forum negativity” happens due to the fact that “when people interact with each other and respond to someone else’s opinion, there’s a tendency to differentiate and try to outdo one another by being more negative.”
Moreover, if we look at the different attributes of a product, it is very common for fans to criticize some aspects of the product while still maintaining an overall positive sentiment and positive purchase intent. One example could be “I don’t like the interface of the new speech recognition in the Galaxy 4,” or “I hate it that you can’t record your calls on the Galaxy.” The writer of this comment, even though expressing a negative opinion about the Samsung Galaxy 4, is actually only criticizing one attribute of it while still liking the overall product, and potentially being loyal to the brand.
Listening to online conversation is definitely necessary for successful brands these days. However, jumping to conclusions based on these conversations is dangerous. Both the theories and the software supporting extraction of online conversation sentiment are, at the moment, far from being predictive. Human involvement in these processes is still highly effective and required, and the overall best measurement of customer sentiments remains sales figures.
In the year 2000, two American researchers (Kathleen D. Vohs and Todd F. Heatherton), performed a Consumer Behavior “torturous study;” they asked a group of participants to watch a boring movie for about an hour. To add onto the torture, they placed a table with snacks in the room – different kinds of chocolate, chocolate cake, chips and more.
The participants were divided into two groups in two separate rooms. One group had the snacks on the table beside them. The other group had the snacks on a table on the other side of the room, so those who wished to have a snack would actually have to get up.
• Before the study, the participants were asked to answer a general questionnaire. One of the questions was whether the participants were watching their weight/dieting.
• The participants probably assumed that they were going to be asked some question about the boring movie later, not suspecting that a key part of the study has to do with the snacks!
After they were done watching the movie, “the torture” continued – the participants were asked to individually solve a geometric puzzle that in fact was not solvable. The researchers monitored the amount of time it took each one of the participants to give up and stop trying to solve the puzzle.
The results were stunning – among the dieters, the group of participants that sat near the snacks gave up on solving the puzzle much faster than the group that was in the room in which they had to get up and walk to the snacks on the other side of the room.
Why did that happen?
The dieters who sat right by the snacks had to employ large amounts of mental power to avoid munching on the snacks. The dieters who sat far away from the snacks, were not in constant eye contact with them, so it was easier for them to resist the temptation and they didn’t have to employ as much “self-regulation.” Among the participants who were not dieting, no difference was found between the groups who sat near the snacks and those who sat far away, apparently since non-dieters did not put significant thought and effort into the snacks.
So, as the theory suggests, when the participants reached the second stage, in which they had to solve the unsolvable puzzle, the dieters from the first group – those who were sitting near the snacks – were mentally exhausted from their efforts to not eat the snacks, and they gave up faster on the puzzle.
Additional studies support the theory that we have a ‘mental power bank’ – our self-control and self-discipline are resources that can get temporarily depleted. On a few additional studies by the same researchers and others, other elements that cause “mental depletion” were tested. Among the things that were found to temporarily deplete our mental resources were: keeping up with hard endurance training, staying alert at a lecture, trying to navigate to an unfamiliar address, and making choices.
For many of us, some of these activities seem obvious – we can feel that decision-making is very mentally consuming – can you recall a time in which you went shopping and felt completely exhausted afterwards? You have to constantly browse and make small decisions between different products, so it is natural to feel depleted afterwards.
While most people can easily see how some activities can be “mentally tiring,” many are not aware of the fact that we have “one mental bank”. What I mean to say is, if you’re going shopping and have to make a handful of minor decisions, it might affect your ability to write a paper for school or prepare a report right after.
Is this knowledge actionable?
On an individual level, it sure is, and once you start thinking of your mental energy as a resource you can engage in better resource management. For example, if you’re going for a job interview or a meeting somewhere you have not been to before, trying to find your way there for the first time right before the meeting may not be the wisest thing to do. If it is very important that you be at your best, you might consider finding the place a day earlier.
On a B2C marketing level, it is important to take into account the implication of mental depletion on your customers. For example, the presence of many options (say the same sofa in many colors) can be overwhelming. The effect of that can cause the customer to give up altogether; or, on other occasions, be less resistant to a salesperson’s pitch. So, like most research findings, the results can go more than one way – analyze the characteristics of your business and your sales funnel to see how you should adjust to the issue of mental depletion.
The ultimate measure of success for any marketing effort is sales figures, purchases, or orders. Since the marketer or researcher tries to get an idea of sales potential before launching a campaign, or in a more isolated manner (attributing an increase in sales to a specific marketing act), purchase intent is used as a predictor of purchase.
Purchase intent should not be considered solely at face value. Although a few studies demonstrate that measures of purchase intent hold some predictive power (Jamieson & Bass, 1989; Stapel, 1971), the only widely-accepted inference is that consumers who report intentions to purchase a product possess higher actual buying rates than consumers who report that they have no intention of buying (Berkman, 1978).
Due to the elusiveness of the concept, many researchers tried to create scales that translate “purchase intent” into “purchase” in order to get a more accurate view of potential sales. Morrison (1979) created a 0 to 1 scale of purchase probability based on purchase intent, and described some systematic discrepancies in purchase intent: the 1.0 group (100% purchase intent) provides no information and should be ignored due to a suspected “yes sayer” effect; the 0.8 group is the more revealing group that should be regarded as potential buyers. These systematic discrepancies vary greatly between industries (Morrison, 1979), thus limit model’s generalizability.
In the online world, however, large amounts of field data are available and used to gauge purchase intent and make potential sales inferences. These inferences remain a challenging task since the online world is connected to the offline world, and many browsing sessions to an e-commerce website lead to an offline purchase that cannot be linked to the browsing session. Another problem in identifying the sales funnel can occur when a visitor performs an exploratory visit from her computer at work, yet completes the purchase using her mobile phone or home computer, leaving the vendor with no way to link the two visits. Maxymiser (2012) Internet Research Agency claims that in 2012, 82% of shoppers have abandoned their shopping baskets; on the other hand, McAfee (2009) Internet security giant claims that 65% of “shopping cart abandoners” returned within two days to complete the purchase. Identifying these returning customers is only possible if the online retailer employs a sophisticated system of visitor tracking through cookies, and only when all visits to the store are made using the same computer and without deleting cookies in-between visits. Thus, deciphering the exact path consumers go through on their way to an online or off-line purchase remains a challenge.
When people buy a coffee at Starbucks, they get more than just coffee. When people shop at Target, they get a different experience than when they shop at Wal-Mart. Individuals describe different experiences when using the Apple’s iPhone or Samsung’s Galaxy phone, even though the functionalities are almost the same.
Both of these examples can be attributed to customer experience, which has always been a core interest for corporations—a good customer experience leads to customer satisfaction, which then leads to customer loyalty and growth (Garg, Rahman, & Kumar, 2010).
Customer experience is a very broad and abstract concept, hence many of the definitions researchers construct lack specificity. When looking at packaged goods, for example, the customer experience often takes place out of the reach of the company, removed from the purchase location. Marketers of packaged goods create messages to convey a branded customer experience via advertising, packaging and other forms of marketing communication (Wyner, 2003).
The customer experience can be separated into a concrete component and a perceptual component; the concrete component is the physical experience created; for example, “Melanie received her package from Amazon after four days.” The perceptual components consist of elements that are harder to measure: the thoughts, feelings and attitudes created in the transaction – for example, “Melanie was pleased when the Amazon package arrived faster than she expected.” The conceptual part of the customer experience depends on countless abstract variables; among them are the customer’s expectations, the customer’s mood while engaging in the transaction, prior attitudes towards the brand, and in-store interaction. Even terms that we perceive to be quite concrete, such as “quality,” are in fact very subjective and ephemeral: “Quality” is often a measure by the extent to which the service or product delivered meets the customer’s expectations (Ghose, 2009), thus even the concept of quality is subjective and relative.
Humans are social creatures, and as such are influenced by others – be it friends or strangers. A few studies dating as back as 1943 suggest that communications among customers were more important than marketing communication issued by the company in influencing product adoption (Rogers, 1962; Ryan & Gross, 1943).
Marketers, aware of these findings, have since tried to harness this property to their service in their quest to create a positive attitude towards brands.
Such attempts can be seen in the use of various social endorsement avenues. Some marketers present expert testimonials to create trust and credibility; others opt for customer testimonials – either real or staged- that are said to positively enhance advertising effectiveness (Martin, Bhimy, & Agee, 2002).
In terms of positive word-of-mouth, Silverman (2001) notes, “getting people to talk often, favorably, to the right people in the right way about your product is far and away the most important thing that you can do as a marketer”. A BuzzMetrics report (Nielsen, 2012) suggests marketers to “Segment high-value customers and treat them as special ambassadors by offering them loyalty programs, member clubs, special offers and the like”, in the hope that they would spread positive word-of-mouth.
Once a customer has an experience with an organization and wants to spread word-of-mouth, the special characteristics of the Internet come to play a major role. As described above, the Internet facilitates a large reach communication channel, which allows for customers to post a message regarding their experience with the brand and instantly reach great audiences. Such large spread was hard to accomplish before the Internet era, when word-of-mouth used to spread much slower – by conversations with another individual or with a relatively small group. New social media referrals are between 20-30 times more effective at driving business than traditional marketing or media appearances (Trusov, Bucklin, & Pauwels, 2009).
This one-to-many communication by the customer becomes a many to many communication once a few individuals start speaking about the same brand – in an online forum, a talkback to an article, or a company’s Facebook page. From there, opinions and experiences can spread exponentially -social media word-of-mouth has up to 30 times larger effects than most paid marketing attempts, with a higher average carryover (Stacey, Pauwels, and Lackman, 2012).
One of the ways in which companies try to harness power of word-of-mouth over the Internet and promote users to spread it, is by placing social plug-ins. Social plug-ins are clickable elements embedded in a company’s websites that facilitate the sharing of content from the company’s websites (or word-of-mouth about it) with the visitor’s network. While a few years ago individuals had to copy the page URL and paste it to an email message to spread it to their friends, today a simple click on an icon in the company’s websites will automatically send the message through the visitors’ favorite social network: Facebook, Twitter, Pinterest, and more.
Even though these plug-ins are efficient and ubiquitous, it is essential to remember that not all word-of-mouth is created equal. First of all, as suggested by prior studies (Godes et al., 2005; Trusov et al., 2009), a lot of the word-of-mouth communication is triggered by a specific marketing action, that should be credited – at least partially – for the surge in online conversations. Second, a study by Stacey et al. (2012) finds that Facebook likes and comments do not significantly affect purchase behavior, and increase website traffic less than topic-specific word-of-mouth conversations.
Asur and Huberman (2010) find in an extensive twitter-based research that message valence (=positive or negative emotion expressed), as opposing to quantity is highly predictive of new movie box office sales; and Chevalier and Mayzlin (2006) find that “more positive” book reviews increase sales, and not just any review. In another online word-of-mouth research the researchers focus on the inherent difference in sentiment that exist across venues, products, and brand attributes (Schweidel, Moe, & Boudreaux, 2012). For example, conversation venues that are one-to-many (such as Twitter and blogs) tend to have more positive posts than venues that are many-to-many (such as forums).
Overall, many studies support the notion that it is not enough to look at quantity of online word-of-mouth mentions, it is crucial to analyze the content – focus on online conversation quality over quantity. Thus, evidence suggests that seeing that a page is “liked” is not enough to create actual liking and trust- visitors have to see specific content rather than something general like a social plug-in suggesting vague social endorsement.