Building blocks for digital enterprises

Every organisation now aims to be digital, innovative, competitive, agile and fast growing apart from being profit-making at minimal cost. Any sane organisation would set it’s sights on the aforementioned targets but the path to achieving the same is tricky. The problem, sometimes, is lack of alignment between the business goals and implementation of it and sometimes (often), businesses are so shrouded with buzzwords that they prioritize being associated with buzzwords rather than building capabilities to achieve business opportunities or solve business problems.

Abuzz with buzzwords

The path to agile digitalization and innovation is carved by identifying use cases based on business goals and thereafter defining the business as well as technical enablers required to accomplish those use cases. One or more capabilities can facilitate one or more use cases, but the mapping exercise needs to be done prior to implementation, bridging the gap between strategic business objectives and capabilities. The capabilities required by a business (the what) tend to remain comparatively constant while, the implementation process (the how) is likely to change frequently. I have come across multiple instances where organizations want to implement AI or chatbots and often without a clear goal or vision in mind, resulting in shadow IT, additional costs and often failed attempts to productionalize the efforts.

Many organisations lack governance, making every new requirement a burdensome task, taking several years to deliver even minute features. I have also come across organisations that have some form of governance, group of enterprise architects, working on process mapping, information modelling etc. but there is a huge gap between the enterprise architecture and the actual IT development. A lack of visibility of the path between purpose and implementation creates silos, making it tedious to deliver enablers for digitalization.

Approach for implementation

Business capability is about identifying what factors facilitate accomplishment of business goals. Be it customer experience, pricing strategy, promotion and distribution of ideas, products and services, the idea is to satisfy the strategic objectives of an enterprise.

Mapping business capabilities to strategic goals makes the business strategy tangible and more visible to the entire enterprise. This leads to a more effective way of using technology to achieve business goals, eliminating enterprise-wide redundancies. A capability-centric organization also helps overcome the common problem of organizational silos.  Visualizing use case -> business capability-> technological capability leverages transparency, integrates, constructs, and reconfigures resources and competences to achieve high performance. The resulting organization is a more agile and adaptable one, leading to faster time-to-market.

Architecting Modern Data Platforms

As organisations struggle to capture and leverage multitudes of data, there is a surge of technological options to choose from. Well designed data platforms facilitate experimentation, have shorter time to markets, have faster adaptation to latest advancements in data technologies, promote self-service thereby accelerating data adoption.  Data being the key enabler for business transformations, it is vital to build platforms that accelerate validation of use cases and can handle scaling of use cases and users. Designing a platform which is elastic enough to embody all the above can be quite a daunting task.

MDA

The primary points to consider when architecting modern data platforms:

  • Customer centric

Organisations battle immensely with legacy data technologies to deliver personalization, and customer experience, despite there being so much emphasis on hyper personalization. Thinking on the lines of creating 360 ° customer view helps align technological choices after business pain points.

  • Cloud Native

Cloud solutions support elastic scaling, high availability  and secure fully managed services with integration to a range of enterprise security systems including LDAP, Active Directory, Kerberos and SAML. Cloud  solutions allow pluggable architecture – replacing components if better options are available with minimum reconstructing. Cloud platforms eliminate the time-consuming work of provisioning resources and infrastructure, thereby reducing time to market.

  • Multi-platform architectures

Be it multi-cloud or multiple data storage patters, it should be the use cases that dictate the architectural patterns and not vice versa. Datawarehouses, datalakes and NoSQL databases can all co-exist on multi-cloud platforms if the use cases demand so. Organisations should avoid platform/vendor lock-ins, because then businesses are forced to make technology choices that are not in the best interests of the company.

  • Microservice-enabled

It is critical to  envision data as not just a means for visualization like a diagnostic tool, data is critical to help organizations adapt to change, in evolving business environments and to innovate and every company wants to expedite the process to be the first ones to come up with innovative products and services. Data plays a key role in this aspect. Monolithic applications are a major bottleneck in this case. In microservices based design small decoupled services are developed completely independent of each other  to achieve business requirements, faster, generally through REST APIs or event streams.

  • Flexible

Modern data platforms should be flexible enough to accomodate rapidly evolving business requirements. Be it integrating new data sources or feeding data into futurist data products. Modern data platforms should simplify testing new ideas on a small scale prior to making heavy investments in infrastructure.

Modernization continues to be a strong trend in data platforms, whether on Hadoop or RDBMS or multi-tenant solutions. It is the ease of integrating new data sources, TCO, prototyping functionalities, security and scaling that matter most in modern platform architectures.

 

Three reasons why Big Data projects fail

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I have not been regular with my personal blog because I have been blogging elsewhere.

Here are the links to my latest blog posts about why Big Data projects fail and how to attract more women into tech.

Having worked extensively in the Big data & IoT space I have closely observed failures over and over again and the reasons for failure being repetitive :

  • Wrong use cases
  • Wrongly staffed projects
  • Obsolete technology

Read the blog post for more details:

Three reasons why Big Data projects so often fail

Being a woman in tech or woman in data I am often the only woman in meetings, trainings and discussions which feels weird. With not many women in tech it gets easier to discriminate the few that do exist. Incidents of mansplaining, gaslighting are rampant and it’s the victim that gets labelled as drama queen while the abusers fo scot free. Organisations that are serious about increasing the number of women in tech need to address glass ceiling, gender wage gaps & bro-culture and cultivate an inclusive work atmosphere. Read my post on how to get more women into tech.

How to Get More Women in Tech

How to become big data – data analyst

Anyone who works in the tech industry is aware of the rising demand of Analytics/ Machine learning professionals. More and more organisations have been jumping on to the data driven decision making bandwagon, thereby accumulating loads of data pertaining to their business. In order to make sense of all the data gathered, organisations will require Big Data Analysts to decipher the data.

  Data Analysts have traditionally worked with pre formatted data, that was served by the IT departments, to perform analysis. But with the need for real time or near-real time Analytics to serve end customers better and faster, analysis needs to be performed faster, thereby making the dependency on IT departments a bottleneck. Analysts are required to understand data streams that ingest millions of records into databases or file systems, Lambda architecture and batch processing of data to understand the influx of data.

Also analysing larger amounts of data requires skills that range from understanding the business complexities, the market and the competitors to a wide range of technical skills in data extraction, data cleaning and transformation, data modelling and statistical methods.

Analytics being a relatively new field, is struggling to resource the market demands with highly skilled Big Data Analysts. Being a Big Data Analyst requires a thorough understanding of data architecture and the data flow from source systems into the big data platform. One can always stick to a specific industry domain and specialize within that, for example Healthcare Analytics, Marketing Analytics, Financial Analytics, Operations Analytics, People Analytics, Gaming Analytics etc. But mastering the end-to-end data chain management can lead to plenty of opportunities, irrespective of industry domain.

The entire Data and Analytics suite includes the following gamut of stages:

  • Data integrations – connecting disparate data sources
  • Data security and governance – ensuring data integrity and access rights
  • Master data management – ensuring consistency and uniformity of data
  • Data Extraction, Transformation and Loading – making raw data business user friendly
  • Hadoop and HDFS – big data storage mechanisms
  • SQL/ Hive / Pig – data query languages
  • R/ Python –  for data analysis and mining programming languages
  • Data science algorithms like Naive Bayes, K-means, AdaBoost etc. – Machine learning algorithms for clustering, classification
  • Data Architecture – solutionizing all the above in an optimized way to deliver business insights

The new age data analysts or a versatile Big Data Analyst is one who understands the complexity of data integrations using APIs or connectors or ETL (Extraction, Transformation and Loading), designs data flow from disparate systems keeping in mind data security and quality issues, can code in SQL or Hive and R or Python and is well acquainted with the machine learning algorithms and has a knack at understanding business complexities.

Since Big Data and Analytics is constantly evolving, it is imperative for anyone aiming at a career within the same, to be well versed with the latest tech stack and architectural breakthroughs. Some ways of doing so:

  • Following knowledgeable industry leaders or big data thought leaders on Twitter
  • Joining Big Data related groups on LinkedIn
  • Following Big Data influencers on LinkedIn
  • Attending events, conferences and seminars on Big Data
  • Connecting with peers within the Big Data industry
  • Last but not the least (probably the most important) enrolling in MOOC (Massive Open Online Course) and/ or Big Data books

Since Analytics is a vast field, encompassing several operations, one could choose to specialise in parts of the Analytics chain like data engineers – specializing in highly scalable data management systems or data scientists specializing in machine learning algorithms or data architects – specializing in the overall data integrations, data flow and storage mechanisms. But in order to excel and future proof a career in the world of Big Data, one needs to master more than one area. A data analyst who is acquainted with all the steps involved in data analysis from data extraction to insights is an asset to any organization and will be much sought after!

Managing corporate innovation

The time is ripe for corporates to embark on a journey of innovation. Having said that it is a rocky road for enterprises that have been in existence for half a century or more, having made hay when the sun shined and thus gathering a good amount of legacy systems and processes on the way. Some organisations that have have chosen to invest in innovation prefer to run the innovation labs separately, far from the core business, the reason being that innovation should not get bogged down by corporate beauracracy. A big part of innovation is experimentation — experimentation with thoughts and ideas and prototyping. Business leaders have a herculean task of ensuring innovation for the businesses of the future while continuously improving the incumbent corporate machinery that generates the revenue necessary for future investments.

Innovation is about inventing new products or services that solve customer needs and can be monetised. Innovation involves trial and error and learning from the same, a structured method of experimentation leads to better tracking of ROI. The pentathlon framework of innovation articulates the methodology from ideation to market launch. The influx of ideas, which form the innovation backlog, can be either disruptive ideas or new ways of resolving existing business challenges.

Each part of the innovation funnel has to imbibe a fail fastand and an iterative approach for further ideation with feedback loops. Every new invention undergoes inception, improvements and adoption followed by stability and subsequent depreciation. If there are many trains of thought in the innovation projects pipeline, there has to be an order in the way of prioritizing the projects. Prioritization of innovation projects should depend on

  • Marketability — is there a potential market for the product/service?
  • Feasibility — is it possible to deliver the project with the resources the company can afford?
  • ROI — How soon is the breakeven point?
  • Time to market — how soon can the MVP be launched?

The implementation of innovation projects are not very different from other corporate projects, involving a portfolio selection based on the prioritization and business urgency. The next stage involves scoping and development, preceeded by prototyping before scaling. Post validation and launch, the impact has to be measured and analyzed to understand the product adoption and customer experience. The insights from measuring innovation efforts lead to newer ideas or incremental improvements to exisiting business processes and/or the innovative product under consideration. There has to be a relentless flow of insights into the innovation funnel to finetune the ideas being considered for prioritization, implementation and launch.

The very existence of an innovation foundry within a coporate house has to be justified to the investors and shareholders. As a means to create a process of accountability there should be KPIs defined, some examples being:

  • Number of ideas considered for prioritization
  • Number of ideas that were productionalized
  • Number ideas that have lead to business process improvement
  • Value added (Value = Accrued benefits — costs)
  • Time to breakeven

The major factors that influence an innovative culture at a corporate level depend on the company culture – the ability to thrive during change and adaptability to new market conditions. It is of utmost importance to recognise and reward the people who contribute to innovation and showcase and communicate the succesful results. Innovation labs within corporates cannot be run separately forever, the outputs from the innovation exercises need to flow back into the day to day business and the challenges from the core business need to be worked upon in the innovation labs.

With innovation being a crucial area of focus for most organisations, there are a lot of ideas floating regarding innovation management. It can be rather chaotic with buzzwords like Big data, AI, Robotics, Augmented Reality etc. being thrown around callously. Technology is a great enabler for business strategy, but great ideas arise from understanding customer requirements and the lack of products or services that fulfill the same.The most significant point of focus in any corporate innovation should be customer centricity and business gain.

Chasing dopamine — The Neo-generalist

Dopamine a chemical released by nerve cells, which plays a major role in reward-motivated behavior. For some of us the adrenaline rush comes seeking and mastering new challenges and then moving onto the next.

Chasing dopamine

This habit of seeking new problems to solve goes beyond job titles, roles and responsibilities, educational background and age. These polymaths, knowledge seekers or autodidacts bloom where planted. They thrive when things need to be fixed, they use the knowledge gained from one industry on others, experience with one method leading to another. Monotonous tasks seem arduous.

“It still holds true that man is most uniquely human when he turns obstacles into opportunities.”–Eric Hoffer

I find it hard to grasp that organisations hire specialists to break down silos to facilitate continuous flow of information between different business units but individuals on the other hand are encouraged to be specialists within a certain discipline. Unless there are people who can handle multidisciplinary roles transcending departmental borders, I do not see a solution to organisational silos. The divide between tech and business is one such area of concern. People with tech skills are assumed to have little grasp of business acumen while people with strong business understanding are assumed to have limited IT proficiency and people with both business and tech expertise are perceived as average in both. That’s due to our obsession with specialism. Generalists are looked down upon, likened to jack of several trades and master of none. On the contrary, the monkey minds are an asset, being able to connect several dots and improvise solutions based on their creative thinking, envisioning paths beyond their job titles.

My constant dilemma

Hailing from India, Jugaad was part of the everyday vocabulary. Jugaad in Hindi means makeshift solutions which requires resourcefulness. Resourcefulness is not part of any syllabus, it comes naturally when there’s a scarcity of means. Being able to do more with less. Doing more has to do with understanding several disciplines to put together a solution beyond frameworks and recognized theories.

I came across a book The Neo-Generalist by Kenneth Mikkelsen and Richard Martin which was sort of narrating my mental state — the nomad state, in search of the next problem irrespective of domain, technological challenges or borders. We, the neo-generalists are happy as long as our brains are being harnessed and we are involved in something meaningful. If you identify yourself as neo-generalist then it is a must read. You’re not alone, there’s a whole tribe of us, restless souls, trying to juggle several disciplines at the same time and loving every moment of it!

Settle down — this word does not appeal to neo-generalists. Constant learning and treading on paths not previously traveled are our only focus.

“Listen baby, ain’t no mountain high,
Ain’t no valley low, ain’t no river wide enough”

Analytics – Implications on Digitization

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Digital is all about data, contrary to the prevalent method of creating Analytics as a silo all by itself. Analytics should be seen as one of the fundamental underlying processes that support the core business processes like product development, marketing, sales, customer relationship, finance and innovation. Data and Analytics provide value to core processes, for continuous improvement.

Most organisations are keen on innovation. Innovation could entail new market opportunities and could be an entirely new value proposition, discovered on a strategy canvas. But innovation could also be a by-product of a business process improvement. Such opportunities can only arise when business processes are tracked, measured and analyzed. Organisations that indulge in hypothesis driven product development or mass marketing could benefit by introducing  a data driven approach to the above processes, thereby uncovering the customer needs and product usage. Businesses may launch products with a certain outcome in mind, but sales, social media feedback and web analytics data may have another story to tell. It is in this story, that new opportunities can be unearthed. Understanding customer behavior is a way of discovering new marketing and/or product/service development opportunities.

Many organisations investing heavily in digitization, charting customer journeys, aimed at improving customer experience across all touch points, seemingly forget to make Analytics an integral part of this process.  The key to understanding  major business drivers like customer retention, ROMI, growth, customer engagement, monetization, finding new customer segments depend on deciphering the business data generated.

Analytics, therefore should be embedded in all business processes to capture the way the end customers perceive products or services or marketing and branding efforts made by any organisation. Analyzing the business data from existing processes could possibly give rise to future business prospects. To tread on a path of continuous improvement and innovation, companies will have to make Analytics a fundamental part of every business strategy.

Future proof your digital career

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If we consider Santa’s career – it has been traditional conveyor belt industry model of mass production. The distribution model is nowhere close to hi-tech, sleighs with reindeers!

Fortunately or unfortunately, Santa will have to embrace the new era of digitalisation to remain in business. Or consumers could turn to AI driven Santas. There is no more a linear career progression path, anymore. Business transformation is heavily data-driven, now, and is constantly evolving, it is of utmost importance for anyone aiming or sustaining at a career within the same, to be well versed with the latest industry trends and technological breakthroughs that will help monetisation of a business idea. Some ways of doing so:

  • Following knowledgeable industry leaders or thought leaders on Twitter/ LinkedIn
  • Joining future trend and related groups on LinkedIn
  • Following influencers on LinkedIn
  • Attending Meetups, events, conferences and seminars
  • Connecting with peers within the industry
  • Last but not the least (probably the most important) enrolling in MOOC (Massive Open Online Course)
  • Updating the reading list and reading the books too 😉

As they say Life begins at the end of your comfort zone.

Continuous delivery of Analytics

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I am biased towards Analytics not only because it is my bread and butter but also my passion. But seriously, Analytics is the most important factor that helps drive businesses forward by providing insights into sales, revenue generation means, operations, competitors and customer satisfaction.

wud-slovakia-2015-datadriven-design-jozef-okay-8-638Analytics being paramount to businesses, the placement of it is still a matter of dispute. The organisations that get it right and are using data to drive their businesses, understand fully well that Analytics is neither a part of IT nor a part of business. It is somewhere in between, an entity in itself.

The insights generated from Analytics is all about business drivers:

  • Performance of the product (Product Analytics)
  • How well is the product perceived by customers (Customer Experience)
  • Can the business generate larger margins without increasing the price of the product (Cost Optimisation)
  • What is the bounce rate and what causes bounce (Funnel Analytics)
  • Getting to know the target audience better (Customer Analytics)

While the above insights are business related and require a deep understanding of the product, online marketing knowledge, data stickiness mastery and product management skills, there is a huge IT infrastructure behind the scenes to be able gather the data required and generate the insights.

To be able to generate the business insights required to drive online and offline traffic or increase sales, organisations need to understand their targeted customer base better. Understanding customer behaviour or product performance entails quite a number of technical tasks in the background:

  • Logging events on the website or app such as registration, add to cart, add to wish list, proceed to payment etc. (Data Pipelines)
  • Having in place a scalable data storage and fast computing infrastructure, which requires knowledge about the various layers of tech stack
  • Utilising machine learning and AI to implement Predictive Analytics and recommendations
  • Implementing data visualisation tools to distribute data easily throughout the organisation to facilitate data driven decision making and spread data literacy

As is the case, Analytics cannot be boxed into either Tech or Business. It is a conjoined effort of both business and tech to understand the business requirements and translate the same into technically implementable steps. Many organisations make the mistake of involving Analytics at the end stage of product or concept development, which is almost a sure shot fiasco. Analytics needs to be involved at every step of a product development or customer experience or UX design or data infrastructure to make sure that the events, the data points that lead to insights, are in place from the beginning.

Delivering Analytics solutions is a collaborative effort that involves DevOps, data engineers, UX designers, online marketeers, social media strategists, IT strategists, Business Analysts, IT/Data architects and data scientists. A close co-operation between tech and business leads to continuous delivery of smarter and faster automations, enhanced customer experience and business insights.

Build. Measure. Evaluate. Optimise. Reevaluate.

 

 

Six Great Marketing Hacks For Startups on a Shoestring Budget

 

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Startups are built around the idea of a product, with entire teams focused on crafting the perfect app experience. But once a product is built and ready for an audience, or if it needs enough traction to secure a round of funding, what should happen next?  The era of “build it and they will come” is over –  to spread the word about the product and harness an audience, marketing is key. Venture capitalists usually look for functional products that customers are already using and a plan to continue growing before investing. Whether your goal is to bootstrap or to raise venture capital for your startup, you won’t get anywhere if you can’t both build it and sell it.

In order to secure funding, startups are required to boast a substantial customer base and in order to be able to acquire customers, marketing is essential. But the good news is that marketing is not all about expensive marketing automation tools and hiring digital marketing specialists. Not only is social media marketing a boon for startups, there are a few more hacks to market the frugal way!

Let’s get started with some of the ways to get the word out about a startup product:

1. A picture is worth a thousand words

Pictures tell a story that are otherwise difficult to articulate.  When it comes to customer engagement, Instagram rules. People sharing the same interest connect through hashtags. Using catchy feel-good hashtags that are associated with the product give a boost to the promotion. For example, if promoting the launch of a toothpaste a peppy hashtag like #Riseandshine will win the toothpaste company many followers as it strikes a chord. Posting often and relevant images — not just about the product but images of related events, products and emotions — will keep the customer engaged and interested. Posting on Instagram does not of course involve money.

French photo printing company Cheerz lets customers easily print their mobile phone pictures from Instagram or Facebook in formats such as Posters, or Magnets. Cheerz use creative and feel good campaigns on Instagram targeting holiday seasons or festivities, to promote their product to print personal images meanwhile inspiring and offering inspiration about home decor with the pictures involved.

Engaging with customers to make them feel part of the product is a sure shot way to win some accolades and loyalty. Starbucks has implemented customer engagement on Instagram by resharing images posted by its fans, earning them goodwill. Here’s an example of Starbucks reaching out to an Instagram user, asking permission to use a great shot of its classic red holiday mug next to a Christmas tree as its Facebook cover image.

A big brand like Mercedes Benz has also turned to Instagram to attract potential customers through a brilliant Instagram campaign to promote the GLA sports utility vehicle by letting the targeted segment of consumers customise and design their new car. The Instagram campaign #gla_build_your_own allows the customers to create their own version of their coveted new car by choosing colour, wheels and roofs. The campaign resulted in increasing the site visits and brand awareness, manifold.

  1. Social Media Marketing

Facebook and twitter are powerful mediums of marketing. Getting customers engaged is the key, by replying to their comments and retweeting. It is, however, imperative to analyze traffic generated by Facebook and twitter to get to know the audience that is interested in the product.

Creating Facebook ads using Ads Manager to target the right audience using parameters to define the appropriate age, demographics, interests and behaviors is quite a low cost marketing gimmick. Facebook also provides an easy way to retarget customers who have abandoned shopping carts or have shown an interest in a product but have not taken the leap to purchase, this post elaborates the steps involved in retargeting customers.

Facebook for Businesses provides many easy to use features for marketing, at pretty low costs. Facebook marketing enables startups to target the right audience by creating custom audiences and boosting posts to increase outreach, geo targeting by promoting products using location based marketing and Facebook also provides insights to be able to measure campaign effectiveness. Here are some great success stories that have been able to create brand awareness and generate revenue using Facebook marketing.

Youtube video advertising is again a great way to spread the word about your product at low costs and it includes features like targeting through customer segmentation and analytics to measure and analyze the traffic..

Analyzing Facebook and Twitter data generates great insights about consumer demographics and sentiments. In order to analyze Facebook and Twitter, open source code like R and Python are freely available on the internet, which when connected to Facebook and Twitter APIs can extract and analyze data regarding customer names, age, geographic location, number of likes, shares , comments, popular hashtags associated with the tweets. One does not have to be an ace programmer to be able to connect to these APIs using R and Python, there are numerous blogs and websites, stating step by step code snippets to connect to Facebook and Twitter APIs to extract and analyze data. Here is an example of steps to connect, fetch data from Facebook and analyze it.

Not only can the Facebook, Instagram, LinkedIn, Pinterest and Twitter APIs be used to generate online customer behavior and help target the befitting audience, the APIs can also be used to conduct competitive analysis by analyzing hashtags that are associated with a competitor’s products.

Social media management product Hootsuite offers a free version to manage up to 3 social networks from a single easy-to-use interface. Basic Analytics, Reports and basic scheduling capabilities that make the life easier for a marketeer, at no cost. Hootsuite also makes it easier to use influencer marketing to promote products by tracking online conversations for hashtags or keywords to spot influencers. Influencers could be buyers themselves but could also very well be bloggers or writers with social influence. When influencers express an interest in a product the outreach is impactful.

3. Utilising trial versions of marketing tools

Trial versions of most marketing tools, are available free of cost for customers, for a limited period. This is not a long term arrangement but while the startup is struggling to get itself noticed, at the same time trying to keep the costs low, it is a blessing. Marketing tools like Marketo, Hubspot, Adobe, Tableau all have trial versions of their tools that can be downloaded for free for a period of 15-30 days, not to forget the free version of Google Analytics. Utilizing the trial version tools also serve as an opportunity to evaluate marketing tools available in the market, that are most suited for the job at hand.

Webhose provides access to data from several sources such as news sites, social sites, blogs and from several different technical platforms with quick integration capabilities, requisites that expedite the new age data driven marketer’s job. Webhose comes with a free trial period which can be utilised to analyze multichannel data sources.

4. Free Market Survey tools

Launching a new product entails verifying that a potential market for the product exists. And if similar products already exist in the market, then it is worth checking information used to identify potential customer segments, opportunities and problems faced to further optimize marketing efforts. Free market survey products such as SurveyMonkey help startups to gather consumer insights and feedback  to optimize their product.

San Francisco based Happy Goat Caramel gained insights about which factors of their product mattered most to their customers by using information gathered through SurveyMonkey. The feedback gathered also helped Happy Goat make strategic decisions about their product and pricing in order to accomplish their growth aspirations.

Market research data that can be fetched from web crawlers or market research companies helps companies gather information about marketing campaigns designed by their competitors. Data regarding the competitor’s ad spend, methods used, SEO techniques can be helpful in creating ad copy optimization.

While working for a media giant, I was involved in a marketing effort where the aim was to increase the market share for the company in regards to online advertising. We gathered data about the big buyers and their ad spend behavior for example if they invested mostly in mobile advertising or print, the kind of advertising – native or branded video, if the method used for ad buying was programmatic or through media agencies, from Market Research companies. This information about customer ad spend gave us an edge over the competitors by being able to target the big spenders in a much more personalised manner through marketing campaigns, thereby increasing the share of wallet. This example can be used for any business case, using market research data to figure out ways to have an edge over competitors marketing strategy can yield only good results.

Data regarding consumer behaviour, preferences, trends, interesting segments are gathered by market research companies, which are usually available, on purchase. But a few government agencies, websites and nonprofit organizations make their market research data open to the general public, enabling SWOT analysis (analysis of Strength, weaknesses, opportunities and threats for a business) saving startups, on meagre budgets, additional expenses.

5. Crowdsourcing content and outreach

Improbable as it may seem but it is very much happening. Startups can engage customers by running contests for the wittiest hashtag and then getting the winner featured on their social media channels. Who does not like fame? Similarly, startup organizations can conduct surveys on Facebook, Twitter and LinkedIn to gain insights about the general opinion about the product. As far as advertising is concerned, customers would feel empowered and a part of the product development journey if they contribute to social media advertising. Considering the toothpaste launching company as an example, starting a campaign by asking customers to post pictures that they can associate with #riseandshine will not only drive more web traffic and create brand awareness, but also contribute to great brand storytelling.

Chaordix Crowd Intelligence process and platform facilitates hundreds of thousands of high-value customer submissions, comments from social media platforms to gather feedback on new products, services and marketing campaigns. Chaordix’s small-business-centric Pro plan costs only $99 per month.

This marketing gig by Unilever to promote Magnum ice creams by letting customers design their own ice creams was a huge hit with customers posting images on social media with a hashtag #magnumstockholm, acting as brand ambassadors.

When it comes to crowdsourcing content marketing – there are numerous talented bloggers and writers that wish for nothing more than a platform to showcase their writing skills, liasoning with such skillful writers to roll out great content on the sites is a win-win situation for all parties involved.

Refuga, a travelpreneur site has a team of crowdsourced writers spread across the globe, writing content for them, not only to showcase their writing to a worldwide audience of entrepreneurs but also winning a free trip per year for adequate amount of high quality articles in return. It is a mutually beneficial collaboration and has worked out well for me, personally.

  1. Marketing swag

To create brand familiarity, giving away branded merchandise at events or as contest prizes or as an incentive is a great idea. But again, it does not necessarily imply a huge cost. Surely there exist startup companies that have tote bags, personalised mugs or USB sticks as their products. Collaborating with such companies to co-sponsor advertising and promote brand awareness, while sharing costs, is a wise thing to do.

New Relic is a SaaS (Software as a Service) application performance management that provides comprehensive, real time visibility into web and mobile applications. New Relic uses marketing swag in the form of their Data Nerd t-shirt which acts as a motivator for their buyers to try the software and deploy it. And of course the subsequent tweets and Instagram images of the t shirts only add to the website traffic volume and brand awareness.

There could be countless other ways to market products on a shoestring that I will add to the list as and when I get brainwaves. Please feel free to share your ideas, views or tips about marketing on a limited budget.