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.

Tips on E-mail Strategy

Tips on E-mail Strategy

ClickZ has recently published an article sharing tips to streamline one’s email strategy.

From a customer engagement process point of view, it is always tempting to collect as much information as possible about customers during the “sign-up” phase. We think this is very risky giving concerns on personal data privacy and the actual personalization that marketers can put together. We don’t give away details when we meet a stranger, do we? This is particularly true in Asia. We recommend improving customer profile as you engage your customers and as you develop capabilities on offering personalization.

Typical email strategy will include a “welcoming” phase and other automated messages such as birthdays and anniversaries. When in use with other behavioral-based triggers, this type of automation is proven to be beneficial in many previous case studies. However, we would recommend special attention to be paid on triggers based on demographic profiles due to privacy concerns. Data captured may not be valid if there is no regular means of updating.

The article highlighted that up to 50% of e-mails are opened on mobile devices. In Asia, our statistics reveal the same trend, if not higher. In China, with a very high mobile internet population it is essential to have “mobile-first” mindset when you conceive any digital marketing efforts.

Therefore, if your e-mail strategy is running on a traditional platform not capable of delivering responsive e-mail, you risk having 50% of your customers having sub-standard experience. It might be a good idea to review your e-mail marketing deliverables before your competition does.

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.


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.


Millennials a Top Target for Loyalty Programs

Millennials were highlighted as a key segment for brand loyalty programs, as reported by the eMarketer research. Nearly 70% of 20 to 34 years olds respondents from US claim that programme rewards can change their shopping decisions. Interestingly, it is not always rewards and points which are attracting these Millennials. Sometimes, it may be because of peer / social group recognition and opportunities to share their products / service experience with others.
In the age of social media, we believe that social networks can be good tools to promote loyalty programs. Providing ways for your customers to gain their social recognition online can be a kind of rewards (intangible but motivating). A simple example of integrating social media with your loyalty program: add share buttons after customers redeem their rewards.

China’s Smartphone Population Passes Half a Billion

Accordingly to eMarketer’s report, there are 9 in 10 internet users used smartphone to browse websites instead of desktop computers at the end of last year in China. The number is still growing and it is very possible that every China’s internet user will go online through mobile phones. This report is not the only report to prove that the population of online mobile phone usage is growing. It serves as another convincing factor to start planning your marketing campaigns with the concept of “Mobile First”. Some keywords you may want to be familiar with: mobile sites, apps, QR codes, NFC, responsive layout.

Retailers Look to Merge Offline and Online Shopping Experiences in 2014

In a November 2013 survey on US digital shoppers by consulting firm Accenture, it showed that up to 72% of respondents had “showroomed” (bought digitally after browsing at a store). Meanwhile, there are 78% of respondents bought in-store after browsing digitally.

We believe this figure is suggesting that both online and offline channels can enhance each other and bring a more comprehensive shopping experience to the consumers. After all, we know that the ultimate goal for retailers to embrace digital solutions is to drive sales.

Facebook Debuts Audience Network and Anonymous Login

Facebook chief executive Mark Zuckerberg made 2 key product announcements: the launching of Audience network and anonymous login.

We had Google’s AdMob and Apple’s iAds platforms dominating the in-app ads previously, but with the Audience Network, there is one more option for marketers to consider when they need to place ads on mobile app. But is this useful given the nature of Facebook is mainly social in nature, while Google and Apple has a more wide spread ads placement channels?

On the other hand, Anonymous Login allows users to decide what data they want to give to an app before signing in. We believe that this may change the data acquisition approach in social media campaigns. This function is still in testing stage and information is limited. We think this is a signal that customer data collection starts to turn to behavioral-focused rather than demographic-focused.