Strategic Overview

The online dating world is in a state of rapid evolution fueled by technological advances and the fragmentation of online dating through new players as fueled by the mobile revolution. JDate, a seasoned player in the industry, is suffering from a declining market share and decreased interest from its target customers coming from culture shifts in the Jewish community and a growing competitive landscape.

Better understanding your niche customer and melding the desires your customers with our marketing and product offering will differentiate JDate in the crowded online dating market. To stay relevant in today's quickly evolving marketplace, it is essential to understand where you stand in the Jewish, and online, dating world, your customers needs / personal lifecycle and having a vision of who you want to be moving forward.

In general, members come and paying members leave. The customer is becoming increasingly fickle and more demanding as newer online dating options emerge.

How do we get them to feel important, recognize the value of the platform and stay with us until they at least find that special someone?

Large segments of our target demographic use technology and date differently than people under 35. They are generally at a different point in life and looking for different things than their millennial counterparts. For them, lack of engagement and connections in their online dating that are the result of quiet communities and diminished interaction breeds a feeling of failure in them and an eventual retreat from the platform.

The mobile revolution has brought about a change in online dating with light-touch mobile swipe apps, freemium models and proximity geographic matching that has created an on-demand dating economy that is disrupting not only the industry but how many of us behave culturally. It is a major change in human behavior that is still in its early days culturally.  

In the past 10-15 years, we have seen the gradual de-stigmatization of online dating as a viable and respected way to find a partner. With the newer mobile swipe generation of dating apps, that stigma still remains to some degree. What are those hidden thoughts and stigmas across JDate various targets with these new models? “Who?” and “How Many?” of our customers view this change negatively and what is the current disconnect for those people. If category evolution continues down this path with increasing popular acceptance, what is the timeframe to widespread acceptance?


The current consumer is older. While JDate wants to appeal to a broader audience, their primary focus is on maximizing the opportunity with the strongest current demographic – customers over 40. 

The secondary (growth) customer segment starts in the late millennial segment and crosses into the primary segment.

The process should uncover a roadmap to expand market to younger and more diverse demographic of serious daters 30+. 

Based on JDate typical age segmentation model, we will segment the surveys with the Primary Customer as M & F 36+ and the Growth Customer as M & F 26-35.

 

The Open Ended questions are important to this particular research as we are primarily looking for context to inform marketing plans that is connected to the reported behavior that helps us understand the customer.

This research assignment is designed to help us obtain context around the extensive research previously done in order to develop marketing, channel, and communication plans. Most of the essential quantitative data points have already been uncovered by Spark. Thus, we are looking to surround the available customer and platform use information with cultural and sociological context to inform a strategic plan and subsequent creative.

In slide 6, the objectives defined are less meant to be data points that we will empirically define and more designed to be concepts that we can justify using data points. In the latter case, we feel we can answer that question using a combination of several data points that will be obtained in this research. For example, we are not as much looking to learn that 70% of our target customer rated feature X as being very important as we are looking to learn 70% of our target customer thought feature X was very important and when asked why, they most frequently mentioned A,B,C. A was by far the most discussed topic with 70 respondents over age 45 expressing concerns. Interestingly, one person said Z which brings up a great line of questioning for Focus Groups and future research.

 

The information and insights provided are based upon a broad research base would it alienate the sub-segment itself? (search for what I already wrote)

The information and insights provided are based upon a broad research base that includes desk, 3rd party and various social sciences research. In some cases, while the body of research supports the claims being made, there has not been an empirical correlation but a transitive correlation between the disparate data sources.

  • It’s commonly said that correlation does not imply causation. That is true (see Gwern’s analysis), but does causation imply correlation? Specifically, if “→” means causes and “~~” means correlates with, does X→Y imply X~~Y? It may seem obvious that the answer is yes, but it is not so clear.
  • Is causality transitive? It seems that the answer should be yes. If A causes B, and B causes C, then A causes C. With symbols:
    • A→B
    • B→C
    • ⊢ A→C
    • (the ⊢ symbol means therefore).
  • If causality is transitive, and causality implies correlation, then we may guess that transitivity holds for correlation too. Does it? Sort of.
  • How does transitivity work for causation? It turns out that it depends on the exact concept we are exploring and is different for each situation. For instance, suppose that A causes higher C and C causes higher Y. Now, we would probably say that A causes higher Y. However, suppose that A also causes higher D and D causes lower Y. Does A cause higher or lower Y? In this study, that is where the social science and demographic research comes in to play. We apply context to the transitive correlations with other research done in the grey areas between transivity.

Please note that should you want to empirically define these insights, we can do so via future quantitative surveys. However, for the purposes of this research, it is less important that 33% of people do something than “about 30%” of people do something. We are not looking for empiricism, but trends and insights into behaviors, and motivations.