I am not close to this space but from what I have experienced so far is the following trends are in visible across the learning tool category - including what my organization is focusing on

• Customized learning needs and analysis
• Self service learning tool set
• Always On - ubiquity of the tool set
• Easy to navigate and intuitive to use interface
• 
  

From my perspective, below is the ethos any service provider should look at
a. making it easier to switch platforms or new personnel looking into the platform
b. K.I.S.S - Keep it simple stupid
c. Pattern isolation
d. Projection/Extrapolation
e. Sourcing Credible data with broad sample sets
f. integration to roadmap tools like Product Plan
g. (Finally) Sharable statistics

In modern RLT, I look forward to identifying patterns and data isolation with globalisation.

Especially pattern isolation with various regional analyses is the success factor for any RLT tools or services.

Research and earning tools are more than data collection and presenting the data! we are always waiting for the WOW moments when we discover something new about our customer or our product.

For any tools, we are looking at what is behind the data? Can the tools identify the correlation between different events? How does the new feature introduce impact another feature?

Just a couple of examples about insights we are looking for, for the winning solution we are looking for

• Focus on insights rather than showing the data
• visualization as a key for all leaders to get more insights into data
• easy to use by all my team - NO expert needed to run reports and find insights

Another key success for any tool is to allow using the insights or analysis and feedback back to customers, in other words, it should be 2-way communication, collecting data from the application for analysis and able to communicate back to users some alerts or notifications based on data

For a company to be competitive in the RLT space, I believe they need to be a company focused on the next step. Alluding to some of my other answers, AI is likely going to be the component that sets companies apart and allows them to data mine and provide usable and actionable information to their customers.

The days of face-to-face meetings are somewhat behind us but the Zoom/Teams "camera on" world we are living in will provide ample ability to gather sentiment analysis not only for customer reaction but for other metrics such as "call length compared to scheduled length" etc. Customer sentiment is never single-fold - and I believe success will come for companies eager to analyze any and all customer's interactions.

For my (current) company, we would prioritize a winning solution based on the following criteria (in relative order of importance):

> ease of use for novice employees
> ease of use for internal admins (ability to configure/edit to keep up with evolving business needs)
> Embedded ability to communicate with customer
> AI components for enhanced sentiment analysis, etc.

Cost is a concern to some degree, as with any tech stack decision.

In the (limited) research I have done for RLT tools, I have noticed AI powered sentiment analysis as a big component to reduce manual work and add predictive resolution paths for reps. I have even heard of this sentiment analysis going to the place of evaluating facial expressions when video calls are available. The robotic learning / automated sentiment components allow the rep (or manager) to further analyze and determine a path forward.

The ability to start slow and add more as you gain confidence or new learnings is key.

A small bite or down payment on a RTL which can then be added onto (bolt on) offers flexibility to the company making the purchase.

It is easier to ask for $10-25k 5 times than $150k once, especially when you are bringing back interesting findings and insights.

Looking through the marketing lens, we need to be better in synch with the zeitgeist and turn around instantaneous responses. Gone are the days of succeeding with materials that take months or even weeks to create. We need a constant voice of the customer pulse check and rapid response thoughts on what's occurring.

The company that will succeed in this space will be able to spin up a response mechanism in hours, and guarantee a steady stream of responses. Let's call it the Reddit of Market Intelligence. To do this, the company will need to create an online community that wants to be checking in daily and finding a way to entice responses. Also, they will need a way to rapidly separate the wheat from the chaff for their clients.

With an increasingly large offer for the customer, solutions that put the customer in the center and really reduce the problem they are trying to solve are the best strategy for a winning product.
The latest trends in outcome-driven development and the pursuit of profit can prioritize functionalities that increase product usage instead of customer satisfaction.

This doesn't need to be a really different list from what is needed to succeed in any product centric organization . The company should have a strong culture of customer centricity , it should keep customer at the heart of everything , have a innovative product with strong support and operational capabilities , sales cycle needs to be tight and self service for majority of customer interactions.

Winning solution would have a customer centric approach , which means it identifies the Jobs to be Done from a client perspective and ensure that the feature set covers these critical areas. Prioritization should be done based on a qualitative methodology which uses a weighted system based on market research and direct client feedback.

The characteristics that I would mostly value in an RTL solution are:
- Relevance to the specific domain I care about: When I do research it is all about getting relevant insights to the specific context I am researching in. Too often RLTs offer generic solutions that cannot offer deep enough insights to be useful.
- Ease of use: This is straightforward, but I should not need to go through a lengthy tutorial in order to be able to use the solution.
- Speed of getting useful insights: Equally important, the solution must be able to generate the first useful insights rather fast

A key element to succeed in the in the RTL space is specialization. RLT companies cannot be one-do-all providers, but will need to pick a specific segment and focus on serving that extremely well in order to be able to compete. Otherwise they will out-competed by other providers that have selected to focus on specific segments. The segmentation can be done on multiple levels e.g. per vertical, per geography etc.

In former times, learning would mean, understanding the information provided, sorting out the connecting dots maybe with the help of a teacher, experimenting and practising, finally being able to use it once and forever.
Maybe driving is a good example. It requires abstraction skills, comprehension and some practical skills. You learn it, sooner or later. From that moment for the rest of your life you are allowed to drive. And you are quite capable doing so even when taking a longer break.

The analogy within RLT could be, yes there is areas where you need to understand, train, and practice until you know it for the rest of your life. Quite unthinkable, that a learning tool however would remain the same for long. They are under constant change. The challenge is no longer so much to "learn" the tool but to have it easily at hand and being able to keep them up to date.

On my laptop I have a lot of folders, including the ones are around research learning. Some I might find within a client folder, some in the research folder, some in my Outlook. With cross-referencing I might be able to remember better where I put the things. But it remains a struggle.

So all these learning tools, and there is certainly plenty of them, the challenge is after being aware of them, to easily access them. Have them at hand, on your notebook, on your mobile. Being able to demonstrate it to others in an easily visualizable manner. Also being able to reference it to other methods or to the inventors, in case there is in-depth questions.

And maybe, this is my personal vision, leveraging search tools that help you to find the things you no longer remember which folder they are sitting in.

I think one of the elements of the learning tool marketplace I've seen (for bespoke research, not a trends library) is speed. A great platform I use is OnePulse, where you can get responses in real time.

The drama that this can create in a client meeting is great, but the real value is in if I've got a question or theory I can validate it in an hour - not a 4-6 week lead time and a cost of thousands.

Fresh, fast insight is vital.

The recruitment of the right people and setting of the culture is a critical foundation to creating a high-performance team and business.

It needs a mix of start/ scale up experts, passionate innovators and also some subject matter experts.

Based on this you can shape the proposition, product and GTM strategy.

Reality is that nobody is going to use an RLT in a vacuum. Their tool has to work in context and in concert with a tech ecosystem. A company needs to provide:

Value

• Demonstrable value through case studies
• In most cases, some sort of free trial tier
• Pricing that reflects value derived

Services

• A spectrum of offerings from DIY through concierge/'white glove' setup
• Benchmarking against similar offerings/solutions

Support

• Initial minimal level of assistance in getting the tool up and running; can't charge extra for this
• Peer-to-peer as well as company-assisted forums
• Different levels of paid support models (SLAs) available
• Cross-platform as much as possible (e.g.: iOS and Android... Azure/AWS/GCP... etc.)

Core characteristics of a winning solution, in my mind, often on the surface come down to how it looks or how much it costs, but that is judging the book by its cover. Considering the plethora of tools out there these days, and the commoditization, the relevancy has to be the top priority. A tool can look great in the board room, but if it doesn't gather and present what you really need in order to understand and make decisions, it's a waste.

Close runners up that add to the overall value/ROI:

• Reusability - the more situations the tool can be applied to, the better it is vs. separate tools
• Flexibility - certain level of adaptation over time
• Modular - ability to understand the components of the tool and take or leave the ones that apply or don't
• Integrations - able to work in concert with other data/systems

In the RLT marketplace, these few trends come to mind...
Specialization, Visualization, and Predictive

Specialization: Because tools are more commoditized and subscription-based, it's easier to offer something that is more bespoke to niche needs.
Visualization: Tools are having to compete on how they help you understand the information gathered in a visual capacity better than another.
Predictive: There is more of an expectation now, with ML and AI, to be able to see 'what if' or 'what will' happen in the future based on current customer data or possibilities selected in the tool's visualization.

Companies successful in the RLT marketplace need to find a way to sell their tools to clients of course. Sure, price is more important than ever. The tools offered need to be easy to use and understand, they need to lead to a narrative that is simple to share and they need to lead to business success ultimately.

However I believe in trust relationships that stand above all in these crazy day. Suppliers that you can trust and rely on. Business partners that not only hand over the tool and the data, but provide a value added and walk the extra mile if needed. That you can call at night if you have a burning problem to solve. That are not shy in spelling out the truth, without surpassing the small path of a client-supplier relationship that is clearly hierarchical, but also has a degree of friendship kind of interaction.

#trust #service #relationships

Characteristics Needed for A Winning Solution:

#1 Ease of Use - Others have stated this as well, but fundamentally it has to be easily engaged by my team or it isn't useful.

#2 Actionable Data - Obvious requirement, but if the data doesn't drive decisions, it is a waste of time.

#3 Ability to Re-Create the Decision Making Context - Recreating the real world decision making environment is challenging, and in some industries will have limitations. But tools that are able to simulate/recreate the applicable product context will generate richer responses. In-person tools/tactics easily lend themselves to creating context, but doing this at scale has been a bit more difficult in the past. I'm hopeful that newer horizons such as AR/Meta will provide novel solutions to get 'in-person' quality information without the logistical barriers.

#4 Easily Adapted to New Demographics or Points of Feedback - As a tool, I want it to grow with my team so it needs to be adaptable and easily altered to new demographics or points of desired feedback.

#5 Intelligent - Able to Identify Weaknesses/Strengths of the pool of respondents. - I want a tool that can see things that I don't. Help me see the Strengths/Weaknesses/Biases of my questions/responses/respondents.

#6 Scalable Pricing - Have a pricing structure that allows access for cash strapped start-ups as well as well resourced large companies. Many tools out there are priced out of reach for young companies who desperately need the product clarity gained from these tools, and would be willing to forgo some of the back end information in order to get critical insights.

There is no doubt that the availability of data now has a positive impact on research and learning. Using proper analytics tools drives more insights into the business and the leaders.

I see new trends now coming to the market

1- No Code - Low Code approach: for many organizations some ready templates and just drag and drop tools you can build your app, connect the different databases and build an analytics dashboard without any code requirements. this will give more power to SMBs to understand their customers

2- Automation: with more data available in real-time now, and with the support of AI/ML, a new trend of automation and customer the user journey based on the behavior. Also, automation will have great support in research to get insights on spot based on customers' action

3- Real-Time analysis: Sometimes purchase data are not enough to understand customer purchasing behavior.

Let me give your one example: In the cinema industry, usually, one person purchases 3-6 tickets for all friends and family. So, based on customers' details you can't build your segmentation about which movie category you like for further marketing and communication. In this case, implementing real-time analysis inside the cinema halls, have a great value to understanding who really attended the movie, with facial recognition can understand the gender, age and even you can go deeper to understand the feeling (like the movie or not) - using real-time data analysis will have a great future in more industries like retail.

Values: reliability, trust, flexibility, capability to scout and find the right data "everywhere", UX (research box)

Services: modular functionalities; capability to customize the specific research; AI exploitation to predict trends; capability to reach the clients through the channels they are already using and are comfortable with

Support: set help desk/support SLA like the Amazon one

• -flexible ->allowing to customize the research for several purposes
• - modular->allowing to buy just the piece for the specific purpose and increase it accordingly to the needs
• -easy to use/UX friendly: with a powerful search engine Google style
• -smart: leveraging AI to predict accurately
• -integrable: easily accessing custom data sources (ie CRM)
• -running as SAAS/BPAAS

Tech trends:

• Powerfool tools (engagements/integration/onboarding)
• AI exploitation to make predictions about the analyzed data
• increase of available data becoming from:
• general digitizalization of business processes
• new digital channels
• sensors/IoT

Economic trends

• specialization of tools; general purpose ->specific for specific markets
• new players becoming from the ICT/Data/AI space (i.e. Google)
• functionalities embedded in existing leading CRM tools
• 
  

For me its about ease of interaction and offering customers a range of options through which they can engage. Different users have different preferences based on their digital maturity so to reach scale, you need to cater for that.
In addition, I see the progressive introduction of gamification ideas into this area. Encourage people to engage and keep coming back through different options, badges, and awards. leader boards etc

Research tools are evolving. What is hot right now, might have not existed back when we were studying them at university. We tend to stick with what we know, but for research tools, this is a mistake. We need to be open minded on new techniques and characteristics that are driving better and faster ways of gathering information. I use a term I used in previous answers, which is unconscious bias. We tend to have a bias for what we already know and what we think works best, but we need to challenge ourselves to try new approaches and embrace technology to obtain better results and insights

There is a continuous evolution of characteristics, but the question is which ones drives the most value? Which ones are the most reliable? For me, the answer is "testing, testing, testing". It's about testing what is working best and focus on creating as much value as possible. If a characteristic/feature is not driving value, it is better to avoid focusing there and focus on where the value is. Only there, we can find a global maximum on the specific characteristic.

What is value? Value is where you can get the best data and for that it is important the ease of use and simplicity. I see so many tools that to appear as "sophisticated" they over complicate the tool itself, making it harder to either get the right data or analyze it properly. Keeping it easy and straightforward can help create more value, both as input and as output

AI and Machine learning are key trends to improve RLTs. Lately there is more involvement to identify and filter (both in and out) customers/users to avoid getting biased replies. I believe we are still at early days of the potential that AI has but in my opinion, it is improving the traditional RLTs.

I have been pitched on RTL which use AI to try and predict customer behavior.

I believe if done well this can work, however, I am worried about the hypothetical scenarios and gathering information in a vacuum. I believe you have to do an enormous amount of basic research to get AI - not sure its worth doing.

Just like fashion changes, so do trends / data / and what you thought you knew.

Companies who succeed will be the ones who ruthlessly retire old assumptions. Think of all the things we 'knew to be true' before Covid. Now, reimagine all those beliefs in this post Covid world.

People need to be in the office to be effective.
Meetings need to be Face to Face.
People need to work.
We can get what we need from our supply chain.

Companies who adapt and accept change the quickest will be the most successful.

Not to go all "Porter," but the only winning solution is continuous innovation and adaption. The market will continue to change and adapt, and if your product and your team is not willing to continuous monitor and adapt you will be left behind.

Winning solutions take the current pulse of the market and provide value. However, there is always someone new in a garage looking to change the landscape and create more value with a new solution.

Current core traits such as integrity, responsiveness, and value will continue to be important. In addition, the ability to listen to and delivery products to niche's seems to be increasing in importance. Culturally, we have moved beyond the 'one size fits all' mentality and favor bespoke solutions where available. Upstream that may mean that the 'Voice of Customer' becomes more plural - 'Voices of Customers'.

One example of this in play is Nike's "Nike By You' platform, where they offer various shoe models that can be customized individually. They have a broad VOC that informs them on which shoe models have the most general interest - but leave the finer details such as colors, fabrics, and even some of the branding up the the consumer in real-time.

In the future, companies that are able to move in-step at niche or even individual levels will be highly attractive.

The RLTs we use tend to be easy to use (easy enough for non-research specialists to pick-up quickly), we require very little in terms of direct support from the provider.

However a provider that 'feels' supportive is a definite plus. Support means being on my side as a user. Sure, be available to answer questions and help me, but also provide an experience that is in my interest - not just yours. e.g. payment options that don't restrict me into contracts or auto-renew.

The RLT I would consider myself most attached to has a community of users we can connect with, which is great for seeing how peers use the tool and get the most out if it. Again, a very supportive experience.

If I can't get a free trial or find a pricing structure that tells me how much the RLT costs ("contact us for pricing" is the worst!), I'll google another RLT provider that does.

There are so many tools out there - and no matter how good the demos are from the sales team - having a free/low cost trial to try the RLT out is the best way of seeing how I would embed the RLT into our VOC programme and give me the best chance of putting together a business case for purchasing for the long-term.

Monthly payments, no contract much more likely to get approved too!

Links to overall ease of use, but incorporating features within voc RLTs that consumers already use makes the process become both easy and more natural. e.g. ability to 'react' or provide answers in emojis, gifs, video files / voice notes are all relatively new features of digital RLTs but many people have been communicating with these for years.

Ease of use is the number one characteristic I would prioritize to make this easy. Case in point, recently I went to a design firm to get some help with a very specific problem, since I'm not working with dedicated UX resources right now. I wanted to conduct a very specific type of study with support of some external resources that were subject matter experts. It took so long to explain the problem and ultimately the proposal I was given was too heavy for us to take forward at the moment. Whatever tooling that is created needs to be easy to use especially for users who may not be familiar with research tools, but robust enough to satisfy the needs of those who are.

Similar to Q2 above, I think ease of use is key. Depending on the market that the tooling is targeting I would recommend providing UX research staffing in addition to a tool so that smaller companies without UX resources that want to take advantage of this can do so as well as a SaaS model.
#successfactors #SMB #startup