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as sent across the U.S. And by the time you’ve finished reading this piece, 90% of the messages sent have been read. If those sentences are all you ever read about the messaging space, likely, they beg the question: What do I need to know to best reach my customers, in the right space, now? Messaging as a Platform (MaaP) is the paradigm shift in the way businesses and consumers are set to interact on messaging.

What isMaaP?

Messaging as a Platform includes all spaces where messaging occurs, from social spaces, like Facebook and Twitter, to messaging apps, WhatsApp and Kik, and, of course, SMS (texting). The space has grown enormously and it’s become a platform with extensive opportunities to create personalized reach. You can think of MaaP as another way companies can build and use apps within a message to reach customers. Though the words “chat,” and “messaging” can be used interchangeably and cause some confusion, the platform for reach encompasses any messaging space, whether it’s branded as “chat” or “messaging.”

MaaP isn’t new, per se, businesses have been courting customer personalization on mobile for over a decade via text messaging with the likes of discount offers, appointment notifications and reminders, or as added security verification. But the means to access users, advancements in how businesses interact and the enormity of the user-base is what’s currently compelling innovation toward apps and marketing within the space. As it relates to SMS, some mobile operators have started moving from SMS when operators rolled-out “Rich Communication Service,” (RCS). With the advent of this service, businesses have more options for increased personalization, though to date, SMS is still the most widely used protocol.

Additionally, messaging apps, such as Facebook Messenger, have opened their doors to third-party developers to create services within messaging apps as a substitute for apps outside of it — such as checking the weather or your bank balance.

With a rich and diverse means of accessing consumers, it can be challenging to know how to calibrate. Starting with these three broad strokes are a best practice approach to adopting a MaaP strategy:

1. RespondQuickly

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, if you’re not shifting from your current Application-to-person (A2P) texting to a MaaP “advanced messaging” technology, you may be missing out on value and revenue, and lose revenue if you continue to interact with your customers solely by A2P SMS. Start by increasing your A2P service and communication strategy now to best position your business and customers for this migration.

2. Adopt the New Paradigm for B2B Interaction

Currently, users are forced to download different apps in order to communicate on each app. What’s more, it can be challenging for new players to enter the messaging space, which is dominated by a handful of brands. But instead of a competitor or vendor mindset, businesses and providers will need to adjust to a more linear relationship so that the Messaging as a Platform landscape is more user friendly for its end-users — customers. With a shift in partnering and access, a brand chatbot used in one space for one purpose, down the road, can be accessed more widely across the platform.

Lest this stroke is too broad, you can think of it as a synergistic system where both platform providers and businesses work concurrently to provide optimum customer experience while still remaining competitive within business models and structures.

3. Start with Either AI or Human CustomerService

Whatever your business model and budget is, entering the space is what’s crucial. Gaining more information on users and user behavior can start whether you’ve added a chatbot to your service or you capitalize on human customer service now. Innovation within the space will continue to advance, but it’s the end-user experience and information gathering here that’s important now.

MaaP is the new direction and ecosystem going forward. This isn’t just because of technological advancements, but most importantly because it lessens friction points in customer experience and increases serviceability.

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A further point to note is that even if you have enough participants, you need to make sure that they’re classified correctly, and put into the right experimental group. Carrying out a study on bilinguals and monolinguals would of course be hampered if it came to light that some participants spoke one more (or less) languages than their grouping would suggest.

classified

This is particularly pertinent in studies examining different mental disorders, in which the grouping definition could be unclear. For example, studies of anxiety may need to differentiate between participants who have been diagnosed with generalized anxiety disorder, or those who suffer from panic attacks, and even if participants exhibit subclinical / prodromal symptoms.

Ensuring that the sample is well-defined, and well characterized, before beginning the study will therefore ensure that the findings are relevant to the group of interest.

Picking cherries, dredging data

While most of the selection biases occur before the data has been collected, there are several steps that occur post-hoc which are open to erroneous distortion. These steps instead to relate to how the data, rather than the sample, is selected.

Cherry-picking is undoubtedly a good way to prepare a pie, but is also the phrase given to the act of only selecting data that conforms with what the experimenter is expecting, or hoping, to see.

Cherry-picking

This can occur due to Pay With Paypal Cheap Price Sale Cheap French Connection Southside Shirt Outlet Affordable Marketable Cheap Online Shopping Online Free Shipping 5G2GK
, or perhaps wishful thinking on behalf of the investigator. Ultimately though, this leads to bad science either way. The investigator must remain open-minded to the contents of the data, and question how they are interpreting things. It may also help if several people (ideally independent) check the data of the study.

Similar to the above, data-dredging (also known as fishing for data, or p-hacking ) is the practice of only considering the data that is significant after the experiment, and inventing post-hoc conclusions for why that emerged. This usually arises when a large number of variables investigated, and spurious results can appear significant.

data-dredging

By taking only significant variables from a dataset, this is essentially the same as running the same experiment multiple times, and publishing the one occurrence in which significant differences were found.

Experimental reproducibility is a particularly important tenet of science that should be maintained when there is a possibility of data-dredging. With enough replications , the research will be shown to be true or false.

Trick splits

Finally, in a similar way to misclassifying the participants before the experiment, their data can be misclassified after the fact. Incorrect partitioning of data is a way of dividing, or not using, certain parts of the data based on false assumptions.

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