How to Execute an Efficient Agile Research Strategy with 4 Steps
How to Execute an Efficient Agile Research Strategy with 4 Steps
Executing an agile research strategy goes far beyond working quickly. Rather, it involves agile software development methods, along with following several best practices. Coupling these key aspects allows businesses to become well-suited to performing agile research.
Becoming a truly agile organization has a powerful impact across departments, even affecting business expenditures. In fact, a Harvard study found that by the end of the agile transition, CEOs spend four times the amount of time on research strategy, at 10% - 40%.
93% of businesses that had fully adopted an agile research strategy before the COVID-19 pandemic outperformed businesses that hadn’t. Clearly, using an agile research strategy is a key component of business operations and success.
This article explains the meaning behind the agile research strategy, its importance, benefits, how to execute it efficiently with four steps, and how surveys are the best bet for software development methods.
Understanding Agile Research Strategy
Agile research strategy is a four-step strategy that falls under agile market research, an approach characterized by accelerating market research operations and processes to ensure timely and efficient results.
There are several best practices that make up this strategy. Since it falls under agile market research, these strategies also involve using agile software and development methods that are iterative in nature.
The iterative approach involves taking small steps towards an end goal, where each step informs the next and involves iterating tests, as opposed to the waterfall approach, which involves completing a project in one go.
The agile research strategy builds off of this iterative approach and can be used in the strategic planning process. In order to truly be agile, this strategy requires coupling on-demand technology with a four-step iterative process.
The Importance of the Agile Research Strategy
Having an agile research strategy is important on many fronts. First and foremost, it is one of the major aspects that uphold agile market research, working in tandem with the agile technology that helps bring any research campaign to completion.
Secondly, this kind of strategy allows researchers and their business at large, to work much faster and become more productive. This is because the agile method relies heavily on using AI and automation market research platforms, the kinds that remove manual, repetitive work from the research process.
It’s also because the strategy involves a great deal of planning, testing, and understanding customers and other research subjects down to a T, as the following section explains.
This strategy allows businesses to perfect a variety of business campaigns, and to do so in an informed and data-backed manner. The agile research strategy works to improve everything from branding and brand visibility to concept testing and customer development.
Essentially, this strategy underpins and strengthens all the processes that are involved in various business campaigns — marketing or otherwise. It helps researchers and other team members better work together, as it typically involves collaborations across departments to reach alignment across goals.
In this way, it brings the democratization of data into play, meaning that all team members can work with data, even those who are non-technical. This is because each step in the strategy is methodical and one designed to help teams conduct and use market research better.
4 Tips on Best Practices for Agile Research Strategy
Brands can execute an informed agile research strategy by adhering to four stages, or steps. This section examines each of these four stages individually, what to do in each, and how these steps ensure quality results.
Foundational Learning
The first stage in the four-step strategy, foundational learning is used for building a strong foundation for the rest of the process. Brands build the foundation via compiling the needs of their target market. This includes their demographics, psychographics, desires, and needs.
Businesses must study their customers directly to obtain this data. They can do so by conducting a focus group, survey panel, or consumer survey. Brands must use the proper market research software or technology to be agile, as these methods can be increasingly time-consuming and difficult to perform in traditional ways.
However a business chooses to construct market research, it should ask questions that allow it to engage in foundational learning. As such, they should tap into areas such as demographics, brand awareness, customer behavior, preferences, and much more.
Businesses can get access to these insights with both qualitative and quantitative market research but must rely on an agile market research platform to deliver these insights.
Innovation Pipeline
The innovation pipeline stage deals with business strategy. This stage should apply the information that businesses garnered from the foundational learning step and apply it across various market research and marketing campaigns.
For example, businesses can use the data they reaped from the first step and use it to build customer personas, tweak their marketing messages, tailor products and innovate on product features, along with broadly connecting with their target market by addressing their needs or speaking with them via representatives.
To do so, brands should identify all kinds of customer behaviors to understand category opportunities and their consumers’ unmet needs. They should continue studying their customers' unique needs at a more granular level, such as with follow-up studies.
Product testing is another crucial element in this stage, as it is still early in the process and the most important iterations occur from survey responses on product ideas and sentiment. Doing so allows businesses to go to market with more confidence that their products meet their consumer needs.
Brands should also validate iterations against competitors’ products, as it will give them a competitive advantage.
When a business reaches parity or superiority in its benchmarks, and consumers detect it, the business will be in a sweet spot and can proceed to develop its campaigns.
Campaign Development
This stage involves developing marketing campaigns supported by strategic research in order to form brand equity, foster awareness, and drive demand. It shows that a good product is not enough to succeed.
Much like creating innovative products, businesses should rely on market research in this step, incorporating things like user panels, foundational learning, iterative tactics, alongside qualitative and quantitative research.
After businesses create campaign ideas based on their target market comprehension, they can test new ads with their consumers. The feedback from this activity is used to iterate and conduct tests. This allows businesses to fine-tune their campaigns and their corresponding messaging, and branding. By iterating and testing, brands can understand the effectiveness of their copy, imagery, and more.
Businesses should continue this work until their benchmarks are outperformed and campaigns are ready to go to market.
Shopper/ Tracking Support
While campaign development is a crucial step in the agile research strategy, it is not the final one. This is because markets and consumers are constantly evolving; businesses ought to remain agile and attentive to sustain a competitive advantage.
As such, there is work to be done after products are tested, iterated, and launched along with their marketing campaigns.
In this final stage, brands should evaluate their shopper and customer support experience. This involves studying the in-store experience, customer interactions with personnel, including those that offer support, along with studying retail assets.
This step is partially used to make forecasts on current markets to ensure effective future campaigns. Although it is impossible to precisely predict markets, businesses can still make accurate predictions by following this four-step strategy. But they must carry out these stages using the correct market research software, the kind that provides agile data.
How Surveys Complement Agile Research Strategy
Given that the majority of agile research strategy involves iteration and testing, surveys — particularly online surveys — are crucial and trustworthy tools to use. They enable brands to extract virtually any kind of customer opinion, sentiment, or need.
This is because surveys allow brands to ask any question and deploy the questionnaire to their target audience. Businesses can use surveys for any campaign, from branding and general marketing to product strategy, and customer experience evaluation. Some online survey platforms even allow market researchers to A/B test concepts concurrently with their surveys.
As such, researchers can iterate concepts with surveys on hand. If customers respond negatively or less enthusiastically than expected to on a concept, a business can test another one by simply deploying another survey.
Surveys can also be used to better understand the overall shopping experience, as described in the final step, as brands can deploy surveys that address their own CX. All in all, surveys can be applied to a variety of campaigns, including pre and post-campaign market research, allowing brands to establish an efficient agile research strategy.
Making Headway in Agile Research
Agile market research is no longer a buzzword, despite the fact that not everyone shares the same definition of it and applies it to slightly different areas of research, along with different objectives.
To reiterate, agile market research is the application of agile software methods (think iterative activities with market research practices, such as the agile research strategy. This strategy dictates how to test, iterate, and launch products, concepts, and campaigns more efficiently and quickly via a customer-centric approach.
In order to facilitate a strong agile research strategy, brands must use an agile market research platform. An online survey platform is brands’ and market researchers’ best bet, as it allows for customer targeting, survey creation, deployment, and post-results data filtering.
A strong online survey platform should obtain high-quality data, which can be executed with the RDE (random device engagement) sampling method. This method engages customers in their natural digital environments in a completely randomized way.
Such a platform must also rely on artificial intelligence to perform quality checks that ensure that brands extract only the highest quality of customer data. These checks should disqualify VPN users, gibberish answers, incomplete surveys, and other sources of poor data from appearing in the final survey results.
When a business uses such an online survey platform, it can seamlessly perform an agile research strategy.
How to Achieve Agile Market Research by Filtering Data
How to Achieve Agile Market Research by Filtering Data
Achieving agile market research is a feat, even for the most technically savvy market researchers. This is due to the vast pools of data that researchers of companies big and small often confront.
Filtering data is both an effective and efficient means of gaining agile market research. This method helps sort out the chaos that bombards even the most powerful of market research tools.
A tool that leaves out critical data categories is bound to increase the presence of survey sampling errors plaguing a market research campaign. Concurrently, a tool that offers a vast amount of data categories and inputs is inclined to tarnish a survey campaign.
Filtering data is the solution, but it must have all the necessary functionalities in order to buttress agile data — and therefore agile market research.
This article explains agile data, how the correct filtering data interface can help you sustain agile market research and how the Pollfish platform offers advanced and granular filtering data functionalities.
Making Sense out of Agile Data
Primarily used in the IT sector and designed particularly for its professionals, agile data is a concept that can significantly improve the market research process.
This is due to the vast reliance on data in market research — be it through secondary means or through the set up of effective survey studies.
IT professionals have founded different methods to accompany the larger catch-all phrase known as agile data. This refers to all the strategies that IT workers can apply to work more in tandem and more effectively on the data facets of software systems.
By fostering the means to work together more constructively, they reap several benefits, such as speed to insight, less waiting on higher-ups for making forthcoming decisions and smoother collaborations.
Agile market research is also borne out of the concept of agile data, to bring such benefits and more into the sphere of research.
The Importance of Achieving Agile Market Research
Achieving agile market research is a necessity in the current information age, in which various digital elements are jockeying for users’ attention, in spite of short attention spans. This ties in directly with survey attrition, along with site and app users avoiding a survey in the first place.
A major deterrent to the survey process, this issue mars the ability to build up an agile system of data collection, analysis and the yielding of results. Some market researchers may create various survey campaigns on similar subject matters as a way to remedy this.
After all, with more surveys on similar subjects, it appears to be more conducive to creating shorter surveys, a common best practice.
However, this runs contrary to agile market research, as it requires more time to create the correct surveys, launch them, cross-reference them and acquire quick results.
Instead, the online survey platform itself must be built on the premise of agile data, so that market researchers can tackle any topic quickly and without the need to implement many surveys and related survey campaigns.
One such way to form agile data and reap its benefits is through an advanced system of filtering data.
How Filtering Data Attains Agile Market Research
Almost every market research SaaS platform offers the filtering functionality, be it for determining the qualified respondents, forming the questionnaire questions and those of the screener.
While the different filtering data systems you’ll come upon in online survey platforms may appear to be carbon copies of one another, a closer look will reveal that they are not. Thus, they do not offer the same prowess of agile data — if any at all.
This is because agile data is not just about streamlining operations, but doing so while providing all the necessary functions and pieces of information.
A potent system of filtering data ensures that market researchers do not forgo using all the necessary categories of data, be it in screening questions, questionnaire questions or the demographics input.
In addition, a strong presence of data filtering allows researchers to organize parse through a large collection of data. This is especially useful in ambitious surveys, i.e., those that are longer or use more skip logic.
When data is neatly filtered, it is much easier to analyze it, make decisions on the next steps and complete a research project in a well-timed manner, thus forging agile data and maintaining agile market research.
How the Pollfish Platform Offers a Top Tier Filtering Data System
Pollfish clients secure agile data on a daily basis through the use of our advanced filtering data system, which is implemented throughout our dashboard, allowing us to divide it into just two sections: the audience and the questionnaire.
This minimalist approach saves researchers the headache and eyesore of rifling through various pages as part of building a survey from the ground up.
Instead, the platform offers multiple categories in our filtering system, permitting each aspect of the survey to be comprehensive and well-organized.
The following explains just how granularly researchers can define both their audience and set up their questionnaire through our filtering data functionalities.
Data Filtering in the Audience Section
First off, our filtering data function allows researchers to reach the correct respondents, with demographics categories that filter through common categories such as age, gender and geo-location. Each category allows researchers to assign quotas, so you receive the exact number of your selected respondents.
Although geolocation appears to be an ordinary demographic option, our filtering system is granular and manifold, so that researchers can filter the geolocation by 9 categories, such as postal code, US Census Region, city, state and more.
There are also 9 categories of demographic criteria, all of which can also be assigned quotas. These include marital status, education, ethnicity, career type and others. Researchers can even filter respondents based on mobile usage criteria and an advertising ID.
To augment all of these advanced filtering options, the Pollfish platform has recently introduced the Multiple Audiences capability, in which researchers can create one survey, with the audience requirements of multiple surveys.
This is because you can create blocks, that is, groups of specific audience requirements and quotas, with each block representing a different audience.
All this smart filtering forms agile data for researchers, so that they won’t need help at every turn, given how intuitive the filtering data system is — and that is just in the audience section.
Data Filtering in the Questionnaire Section
The questionnaire section includes multi-pronged filtering data capabilities. The filtering options span various categories so that researchers can cover all bases in their studies. This also opens up the opportunity to use just one survey per campaign.
Firstly, researchers can select the kind of question they seek to use, with 8 options of question types available (single selection, multiple selection, ranking, etc). These form the core of the survey type, in that they can take the survey in various stylistic and thematic directions.
For example, there is an option for an NPS question, the heart of the NPS survey. Or, you can use a ranking question to create the CSAT survey. The ratings stars question option allows you to create a visual ratings survey, specifically one that uses stars and so on.
After you choose the question type, there are 7 categories you can use to filter the answers. For example, you can add media to an answer, such as an MOV file, a GIF, an audio file, etc. Or you can apply logic, which routes respondents to appropriate questions based on their answer to a question of origin.
When researchers are at a stumbling block in terms of answer options, they can use the predetermined answers filtering option. This filtering data function is rich in categories, offering 46 scaled answer options. For example, researchers can use answers using a scale of disagree to agree, satisfied to dissatisfied, far exceeding expectations to falling short of expectations, the days of the week and many more.
The magnitude of filtering options will prevent any researcher-based writing block when it comes to crafting answers.
There is also a category of “none of the above,” which can be accompanied with an image. There is an “other option,” in which you can have responders specify their answer. This too can be paired with an image.
Instead of manually changing the order of answers, you can use the “shuffle answers” filtering data option. Or, you can implement “batch answers,” a function that allows you to paste all of your answers into the answer portion at once, should you decide to use them from an external document.
A smart system driven by AI, Pollfish divides the pasted content into separate answers. This allows you to avoid copy/pasting each one manually, as they are all inserted automatically.
All in all, the Pollfish system of filtering data is an advanced system of granular categories and selections, which facilitate the survey creation process, in turn providing agile data that is feasible to interpret.
Filtering Data in the Results Dashboard
Finally, and perhaps most importantly, data filtering is applied in the post-survey results in the Pollfish dashboard. There, researchers have the option of filtering data to their liking for all of their analysis needs.
When filtering data in the results dashboard, researchers have filtering options on the left panel of the page. They can sort their results by selecting their desired filters and deselecting the rest.
They can also filter by question and answer to see how respondents answered a certain question, by clicking on their desired question or answer. They also have the option to export the filtered results by clicking on “exports” and then instead of “all data” selecting “current view.”
There is also a “time range” filter on the left panel, which can also give them a grasp of the survey distribution in real-time.
Additionally, there is a post-stratification filter; this filter weighs the age and gender demographics to match the census data of the targeted region.
The results are adjusted to reflect this change. This is why the filter on each response counts differently (hence the number/ percentage discrepancy when the filter is enabled). This data is a more accurate representation of the targeted audience.
The platform also allows researchers to download various documents for analyzing post-survey data — data that has already been extracted by Pollfish. Researchers can download this data in four different kinds of report types for all kinds of analyses.
The following explains the 4 kinds of data exports available for data filtering:
- PDF: A visual document that can easily be shared with stakeholders and saved as reference docs. A Pollfish PDF is laid out similarly to a PowerPoint presentation.
- Excel Spreadsheet: Recognizable to most businesses, with a Pollfish spreadsheet export, researchers have full access to all Pollfish survey results. They can add pivot tables, graphs and get deeper insights.
- Crosstabs Report: Crosstabs are a matrix-style data visualization format and one of the most useful ways that market researchers analyze data. This kind of report allows researchers to look into individual insights and organize the data in different ways, opening different consumer insights that wouldn’t be readily available from the initial results.
- SPSS Report: SPSS is a set of software programs combined in a single package. It allows you to add your Pollfish results to various kinds of complex data analyses. It’s good at combining varied, complex data sets. Researchers use it to make connections, find correlations and graph results from various data exports at once. SPSS has several tools to analyze data for predictions and spotting patterns, increasing its use for brands and marketers looking for buying behavior trends or to vet the viability of a new product.
The Gateway to Nurturing Agile Market Research
Obtaining agile market research is never an easy task — that is, if you’re using a below-par online survey platform. There are several ways such a platform can spur agile data, such as via a mobile-first approach and with powerful SaaS integrations.
Additionally, filtering data is a critical component of nurturing agile data for market research. As one of the main elements of a market research tool, it is not universally equivalent across platforms. This function tends to differ from platform to platform, as such it is not always conducive to agile data.
On the contrary, the Pollfish filtering data system encompasses all aspects of the survey creation process. It is built as a means of providing comprehensive coverage of all categories, whether they pertain to the screening section, aka, the audience section, or the questionnaire.
It is also very intuitive, thus molding agile data by its very structure.
Frequently asked questions
How do you use data filtering in the questionnaire section?
The questionnaire section includes multi-pronged filtering data capabilities. The filtering options span various categories so that researchers can cover all bases in their studies.
How many data exports are available for data filtering?
Four main data exports are available for filtering data: PDF, Excel Spreadsheet, Crosstabs Reports, and SPSS reports.
Why is filtering important in agile data market research?
Researchers often divide their data into different categories for a more comprehensive understanding of it. Filtering this data opens up the opportunity to use one survey per campaign, which can zoom in on each category in a better way. Additionally, filtering data allows for easy organization.
How does the filtering data system work?
Clients can use an advanced filtering system post-survey results to divide it into two sections: the questionnaire and the audience. It also allows clients to divide their data into multiple categories for a complete and elaborate survey result.
How do you filter data in the results dashboard?
There are filtering options on the left panel of the page where researchers can sort the result by choosing their desired filters. For example, they can also use filters to view how respondents have answered certain questions.
How SaaS Integrations Help Sustain Agile Data for Market Research
How SaaS Integrations Help Sustain Agile Data for Market Research
In today’s mobile-first digital age, market researchers would be hard-pressed not to find SaaS integrations and solutions designed to carry out market research campaigns.
Given the efficiency that SaaS brings organizations, a colossal 94% of businesses already use SaaS products. Sustaining a compound annual growth rate (CAGR) of 16.4% from 2017 to 2022, the SaaS industry is not at risk of undergoing a slowdown — on the contrary, it is slated for growth.
SaaS has also progressed into the market research space, with the prevalence of online survey tools, platforms and integrations.
While it is undoubtable that SaaS offers value to market researchers, not all SaaS solutions foster agile data.
In keeping with our stance on agile data for market research, this article explains how SaaS integrations can forge agile data, with a real use case example from one of our clients.
Defining Agile Data
Agile data refers to a variety of techniques traditionally used by IT professionals to ensure effective collaboration on the various data aspects of software systems.
As opposed to entailing a uniform approach, agile data employs several techniques and philosophies to allow for efficient and productive cooperation when dealing with software systems.
Although smooth collaboration appears to be a self-evident necessity — therefore not needing the concept of agile data — in reality, it is very difficult to achieve. This is due to the different role specializations, visions and priorities among IT and data professionals.
Thus, agile data is a crucial concept to incorporate into software systems so that teams have a stronger means of working collaboratively and quicker speed to insights.
The same notion applies to agile data in market research, given that it too relies on SaaS and copious amounts of data.
The Need for Agile Data in Market Research
Market research requires agile data solutions in order to keep up with business needs. This entails access to accurate data on target populations through efficient means.
In market research, such a population is often the target market, the group of consumers most likely to buy from a business and are thus the target of various business endeavors such as advertising, user testing, etc.
Businesses pour so many investments into their target market, thus, the stakes are much higher than a traditional research project, as there is a heightened requisite to acquire an ROI. Thus, the data that market researchers extract must be above par.
But agile data in market research does not merely represent the results that a market research campaign has yielded. Rather, it requires the means of extracting the data in the first place to be agile as well.
As such, SaaS solutions in market research must offer agile data aggregation and agile interfaces. Most market research SaaS exists in the form of an online survey platform, given that effective survey studies provide a vast array of insights for market research projects.
But not all of these survey platforms are optimized for agile data. There are several ways for a survey platform to provide agile solutions. The first was via the aforementioned mobile-first approach (link above).
You can also gain agile data through the use of SaaS integrations. That way, you are not limited to relying on a survey platform on its own.
How SaaS Integrations Build and Strengthen Agile Data
SaaS integrations buttress various business endeavors, including those of market research. This is because using SaaS integrations with a main solution or even in tandem with smaller solutions, strengthens your market research campaigns with an ecosystem instead of a lone wolf market research platform.
The addition of integrating your existing SaaS solutions in your market research certainly has its advantages. SaaS products are built to cultivate agile data and provide other advantages as add-ons to your main SaaS provider.
In order for an online survey tool to gain agile data, its SaaS integrations must advance the key efficiencies found within agile data. These include:
- Easing collaborations
- Enhancing the features of your online survey platform or other market research SaaS
- Using only the aspects of each software that you only need due to the presence of more than one SaaS
- Identifying course changes more quickly, and holding market research directly accountable for business results.
- Organizing data for practical survey data analysis
- Accessing information sooner
- Improving the quality of data
- Garnering further insights delivered at speed
SaaS Integrations Use Case with a Pollfish Client
There is a vast amount of available SaaS integrations for market research products, even if they are not all built the same and do not offer the same functionalities.
In order to generate agile data, market researchers, business owners and marketers need to employ the correct SaaS integrations, particularly those that help researchers gain the benefits laid out in the prior section.
To prove that SaaS integrations can build agile data for market research, the below explains how a Pollfish customer was able to do just that: use a SaaS integration to sustain agile data for their market research needs.
One of our clients in our vast clientele pool is an audio streaming and media services provider.
The audio streaming provider has multiple accounts on the Pollfish platform, including a main account, which runs surveys across the world, in over 15 countries. The main account is used for various market research needs, which include:
- creative testing
- ad testing
- push notification concepts
With 9 users on the account and various survey campaigns in the works, the streaming provider is in constant need of agile data. There is little room for error in the main account of such a major enterprise; it therefore has the necessities that only agile data can provide.
These exigencies include:
- speedy insights
- ease of collaborations
- enhanced data organization and displays
- quality data
- a smooth integration
The audio streaming client was able to fulfill these needs through an integration with BigQuery on the Pollfish platform. BigQuery is a serverless warehouse on which the client was able to build a massive dashboard.
This SaaS integration allowed the client to store, segment and view the data that it extracted from Pollfish in their BigQuery dashboard. It enabled the client to route all the Pollfish data into the BigQuery database in real-time. This includes survey demographics, respondent profiles and the questionnaire content itself.
Pollfish was able to provide agile data through its platform’s capabilities, along with the friction-free integration with BigQuery, which allowed the media client to view and segment their Pollfish data.
Additionally, the client was able to keep track of several performance metrics, receive translated responses from Pollfish and was able to benchmark their metrics on a rolling basis.
All in all, the media client was able to quickly and efficiently make use of their data via a SaaS integration on the Pollfish platform.
Striving for Agile Data in Market Research
The agile data (AD) concept is a method involving various strategies for IT professionals to implement in software systems. Used in different situations, agile data is used to improve collaborations and other kinds of productivity, while avoiding snafus.
Agile data is a must in market research and other data-heavy industries — and most industries must rely on consumer data in order to build customer loyalty and remain competitive. Thus, numerous sectors can stand to use agile data solutions.
In market research, the key is to choose an online survey platform that can provide agile data — in a proven way. SaaS integrations are one of the ways in which such a platform can remain agile, that is, if the integration itself and the integrated platform permit agility.
Frequently asked questions
How can agile data make market research easy?
Agile data improves collaboration between data sets and surveys and makes collecting information more efficient and productive. Therefore, it is vital to choose an online survey platform that provides agile data for market research. One such way is using SaaS integrations.
Why is agile data market research important for industries?
Agile data is a must in market research and other data-heavy industries since all industries require consumer data to build customer loyalty and remain competitive. Furthermore, agile data makes it easy for industries to collect data from different platforms, increasing collaboration. This also eliminates repetition and any chance of errors. Thus, numerous sectors stand to use agile data solutions.
What key efficiencies should Saas Integrations have to strengthen agile data?
A few ways that SaaS Integrations can make the collection of agile data easy are by enhancing online survey platforms and easing collaborations between data sets and survey platforms. It can also organize data quickly for practical survey results and analyze business results directly through market research surveys.
How can Pollfish's SaaS Integrations help clients with their data?
SaaS Integrations allow clients to store, segment, and view all their survey data stored in the BigQuery dashboard in real-time. The dashboard includes respondent profiles, survey demographics, and the questionnaire content itself.
What is agile data?
Agile data is a data collection technique used by IT professionals to ensure coherency between various data aspects of software systems.