Utilize Social Data or Ignore It

No one will doubt the power of social media, but not every marketers knows how to make a social media campaign successful and effective by making good use of social data efficiently. A recent research also indicated that there are 39% marketers believe that social data is not yet useful to their business.

The main reason may be the posts and comments in social media are highly qualitative data which require professional expertise and resources to analyze. Although some sentiment analysis1 tools could help, limitations still exist. Most analysis algorithms use simple terms (i.e. keywords) to assign sentiments of a product or service, the context, cultural and linguistic factors will be eliminated in analysis. Even sentiment analysis using algorithmic approach does not yield promising insights.

Seemingly, we can’t find the key to unlock the social data to actionable information. We come up with one simple equation may help — Brand mention + location + time + people. For example, travel plans and certain airlines are frequently mentioned in social media by universities’ students during summer holiday. The airline therefore could make sales opportunities at campus right before the summer holiday starts. They could also base on the popularity of destination mentioned to adjust the supply of different routes and offer different packages. The real time customers’ opinions and reactions on social media help you understand how people respond to your brand and marketing initiatives.

It may be hard to deal with the social data as we can see the current sentiment analysis is not yet ready (at least for Chinese/Cantonese). Do try on other approaches to discover insights, but do not ignore the valuable social data. We are looking forward to hearing your success in utilizing social data.

1 Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials.

Read more:

Think twice when we deploy algorithm-based marketing

While we are excited to follow the emerging big data trend for providing smarter, more relevant and personalized content to our customers, we must be careful to decide when, where and what are we showing them. From a recent HBR article, we all can learn a lesson from Facebook’s bad example: their “Year in Review” app showed a father his dead daughter’s photo last year and being bombard by public on their “thoughtless” design.

What happened to this tech giant suggested that our marketing algorithms may still not be sensitive enough to context. To let our digitized engagement activities to be more “humane”, a wider variety of information that matters (affects how the target audiences perceive the content) will be needed. In the case of Facebook “Year in Review” app, the algorithm might need to include semantic analysis so that the system may know to avoid posting a sad memory of a person. We would like to pay special attention to occasions that might easily trigger people’s negative emotional responses, as reminding people of their unhappy experience not only adds no value to the business, but also ruin the relationship between the brand and customers. To start with, you may proactively check if customer would like to update their personal profile on your database annually (e.g. if one of them get divorced, you will not want your system to send them wedding anniversary promotions).

Besides educating our systems to be more “considerate” on selecting content, we shall also need controlling the level of smart interaction to prevent creeping someone out. Privacy is always customers’ concern. It is important to make our customer trust us when they know what we know about them and be well informed on how we are going to use these data. We might be collecting data from various channels while planning to make good use of these efforts in the marketing strategies, but we shall also beware of “surprising” our customers with an unexpected personalized experience.

Here are a few useful tactics suggested by the article that can help:

  • Field experimentation and observational research:
    Testing the impact of different factors and generate more flexible algorithm
  • Introduce unpredictability:
    Avoiding customers being habituated to regular marketing activities by adding excitements / surprises
  • Identify key customers decision and experience points and encourage human interaction:
    Combining the power of technology and flexibility of human to discover new needs or handle exceptional situations

Are you collecting data now for your next smart campaign? It can be very interesting to embrace algorithms in our marketing strategies and it worth for a cautious planning to start. Make sure you consider your customer’s point of view.

Hotel Marketing indicator – Online Reputation Management

Online reputation has become a key success indicator for marketers. For hoteliers, online reviews not only give the hotel management team insights into their operation performance, but also influence their revenue. A recent study by TripAdvisor states that while pricing is still the most important factor in accommodation booking decisions, reviews & ratings on review sites come next. As travel planning is becoming more dependent on online reviews and ratings, hotel owners are likely to increase their investment on online reputation management.

Marketers may refer to the following to start a successful online reputation management effort:

1. Review readiness: To determine which platform to engage (i.e. social media, forum) by studying relevant case studies and hear from practitioners.

2. Map out strategy: To define and prioritize objectives and goals. Examples to spur your thinking:

• Drive incremental bookings and increase revenue
• Improve ranking on TripAdvisor
• Increase guest satisfaction and loyalty
• Influence and engage in online conversations about your hotel

After defining the goals, establish a governance policy by identifying opportunities and risks.
Set relevant metrics. (# of comments, customer Satisfaction level, complaints vs. complements, time-to-follow up on actions, # of replies.)

3. Engagement: Analyze guest by developing personas based on behavioral information. Read hotel’s reviews from the top online (TripAdvisor, Hotels.com, etc.) in order to understand their needs. Reply to online reviews in a tone & manner that reinforce the brand.

4. Evaluate outcomes: measure and analyze the data.

5. Acquire feedback and learning: Regular meetings to highlight the key learnings from the outcomes and refining the strategies and measurement.

And the cycle repeats.

Read more: http://www.emarketer.com/Article/Hotels-Reserve-Spending-Online-Reputation-Management/1012282/7

You next big step to a more personalized marketing strategy

How familiar are you with personalized marketing? Did you try greeting customers and providing them relevant marketing content (e.g. email promotions) based on their profile in the lead database? Did you try segment them based on their transactional or behavioral data then engage them in customized campaigns?

We talked about the importance of personalization in our marketing campaigns in numerous articles and we believe the industry is fully aware of how personalization can help them win in the competition. Now, we would like to take a step forward to explore how automation can help us utilize the power of our consumer’s data.

Last week in AWS Summit in San Francisco, Amazon announced a new machine learning service which provides some visualization tools and wizards that can guide us to use the machine learning technology. By using these tools, they claim to be able to help the market building smart data-driven applications that can do an exhaustive analysis of past/real-time data, and also predicts what may happen in the future. We can imagine a lot of use cases on personalization for marketing, for example:
1. Optimize website content / flow based on customer actions
2. Use prior customer activity to choose the most relevant email campaign for target customers
3. Classify reviews using semantic analysis for providing more insights
4. Identify potential customers / customers who have high risk of attrition for plotting more effective engagement strategies

We are excited to hear this news given that one of the tech giants is leading this effort to make machine learning being more accessible in a lower price. But as this is still new to the market, we also believe it takes some time for us to learn how we can use this machine learning tools maturely.

Let’s celebrate this good start and look forward to improving our ROI by this kind of evolving new technologies.


Insights to consider enhancing your customer engagement process by self-service Apps / Kiosks

Have you tried entering a restaurant and making your order with an iPad? How do you feel about that? Will it affect your decision on making orders?

When automation starts to play an important role and efficiency optimization provides opportunities to enhance customer engagement journey, it is high time for us to think how we can utilize this to lower cost and increase revenue.

An article published recently in Harvard Business Review discussed how these self-service apps and kiosks are changing customer behavior. Some examples in the F&B industry did record an increase in sales after digitalizing their customer services. An interesting example from a liquor store: after they introduced self-service technology, the market share of difficult-to-pronounce items increased 8.4%. This change may be caused by the reduction of social friction, customers no need to worry about any negative judgment.

However, the research also reveals the downside of using self-service technology – customers satisfaction may fall when they cannot see that effort is going behind the scenes. This situation is happening particularly in the banking industry. Additionally, technology is often standardized with a set of rules that lacks flexibility. It may disappoint our customers if thing goes wrong under a new situation and we cannot react simultaneously.

So, is your business suitable to implement self-services technology? Here are some key questions to consider:

  • Do your target customers know what’s expected of them (what steps they need to go through)?
  • Are they capable of doing what’s expected of them?
  • Can customers see the value of paying extra effort for self-service so they are willing to change their way of engagement?

Discussion with your team and working on prototypes may help you to find out more.

We shall share with you more insights on how technology can help you improving customer engagement in upcoming blog posts. Stay tuned!

Source: https://hbr.org/2015/03/how-self-service-kiosks-are-changing-customer-behavior

Harness the real power of big data with interent of things

Imagine how much data our world is creating every 10 minutes today. An article from ClickZ reveals that the amount of data we created before 2003 is now equivalent to what we create in every 10 minutes. We shall not underestimate the potential of big data on improving customer engagement.

For example, you are on a car and your location information is made available to an advertising agency. They will know when and where to show you an ad on the small screen in front of your seat that you might be interested in during your journey. Re-targeting can happen combining offline element and happen almost in real-time process. This example may sound a little cutting edge, but we think it is entirely feasible when the infrastructure is there.

However, does it mean that we cannot make use of the big data with internet of things before any industry leader has built the infrastructure for marketing? We don’t think so. Recall how we make use of website traffic data to improve the performance of this online marketing channel. We could know and capture the identity, activities and location data of our visitors (i.e. Who are they? Which page did they visit? Where did they come from? What did they do?).

We can try to leverage this methodology to unlock the value of data we collected from a wide array of interactions offline – if a customer joined a hotel group’s loyalty programme, their F&B transaction history and interest will be made available. When customers visit any one of the hotels under that group, we can present a customized room leaflet (digital version) to them, providing him/her with more relevant stay options, tour information and deliver better customer experience.

Do you have any idea on how we can work with big data on the internet of things? Please share with us.

Reference: http://www.clickz.com/clickz/news/2391672/what-does-big-data-mean-for-the-internet-of-things

The power of retargeting

Given the emerging trend of digital marketing to be more data-driven, we believe “Retargeting” is an important concept for professional marketer to explore. If we would like to explain the concept in a simple sentence, it can be: Retargeting can bring the right ad to right people at the right place. It increases effectiveness of targeting to interested customers, thus drives sales, improves the brand recognition and overall ROI on marketing.

So, how does it work?

Agencies or businesses pay to the ad group networks to get higher exposure on their promotion content. The ad group network can then display relevant online ads on the website to visitors who browse related websites within their channels. Retargeting identifies visitors by capturing cookies which enable ad group network utilizing demographics and browsing history data to draw potential customers to websites. For example, the ad group network can display a London Hotel’s promotion web banner if you recently visit a lot of London travel information websites.

We can see a growing importance of retargeting. Marketers now adopt behavioral data as well for accomplishing various marketing purposes such as dormant customer re-engagement and delivering cross-sell/upsell campaigns.

Retargeting can be done in various channels such as social network, websites, mobile apps, and email promotions. Social network is the most popular retargeting channels among the above as recent research showed that it drives almost 3 times more impressions and clicks. Take the leading social network platform Facebook as an example: they always display ads relevant to you when you browse your news feeds.

We shall share more tips on retargeting in our blog soon. Stay tuned!

Read more: http://venturebeat.com/2014/12/16/90-of-marketers-say-retargeting-now-as-good-as-search-ads-email-marketing/

Fashion industry embracing new technologies

While there are traditional fashion brands that have yet to adapt internet and new technologies, a slow but steady transformation had started in the industry. We can see brands like Dolce & Gabanna has begun to start marketing digitally, with the assistance of an agency, Fashionbi. Fashionbi has started 3 years ago with an initial goal to encourage Italy’s fashion brands to embrace digital marketing strategies. They help analyse brands’ social media marketing performance and how customers behave on e-commerce sites, e.g. how long they stay on one product, how many products they browse, which are the most popular products, etc.

This type of analytic model can be applied to other industries as well. As customer behavior online changes, we believe a comprehensive analytic model will be beneficial for brands to gain deeper insights on what customers want and deliver more relevant messages to them, delivering a more engaging customer experiences.

Read more: http://read.bbwc.cn/content-1-1042-244-10050258.html?utm_source=mail

Apple Gets An Exhaustive iWatch Patent | TechCrunch

Apple is jumping on the wearable technology bandwagon finally. It will be interesting to see what Apple will bring to this fast-growing market. Tech-savvy consumers have already come to aware wearable technology, thanks to Fitbit, Sony, LG and Samsung who have invested a lot of resources on the first generation smart bands and watches.

We see the next generation of wearable technology to have several exciting characteristics:

(1) more use cases through additional sensors
(2) better interpretation of data captured from the user & the environment
(3) an open platform for 3rd party developers – eventually becoming a new ecosystem
(4) marketing opportunities for brands based on the new form of data captured
(5) new form factors (i.e. not just bands & watches)

The Content Marketing Revolution

A recent report from the Content Marketing Institute suggests majority of audience would rather learn about a company via an article than an advertisement. Content Marketing has become a buzzword for digital marketers. The skills to tell good stories and produce appealing content are essential success factors for a brand to market themselves. In SEO, given the change of search algorithm in Google (focusing on semantic search), high quality and relevant content can help effectively increase exposure. We can see that the e-marketing channels are evolving to an intelligent direction where content can be analyzed and exposed to audiences in a smarter way. We believe that “Content Marketing” will become more important later on e-marketers agenda.