Create a Location-based Marketing Campaign with Geotoko

Location-based marketing is one of the major trends in the digital advertising world.

Instead of increasing web traffic and hoping that customers will visit and make purchase, location-based marketing directly helps your business increase foot traffic, bringing consumers closer to your physical point of sales.

However, with so many different location technologies like Gowalla, Foursquare, and recently Twitter and Facebook Places, location-based marketing has become a complex issue for business owners and advertisers.

(1) As advertisers, we first have to identify the campaign’s goals and define our target audience. (2) Second, we have to decide which location-based technology is best to reach our target audience. Advertisers usually just pick one location platform because it can be quite a challenge to manage multiple platforms. (3) Third, we have to understand how the technology works to ensure the campaign idea can be performed, tracked and analyzed. It’s a hell load of work and this complexity hinders the adoption rate of location-based marketing.

Geotoko, a location-based marketing platform, has simplified the process. Using Geotoko, advertisers are able to manage, track and analyze multiple check-in technologies, all on one platform. You’re just 6 simple steps away to create your very own location-based marketing campaign. If you have all the details sorted out, it will take you less than 5 minutes to create one. Step-by-step explanations below:

Step 1. Key in campaign details

This step is mundane but crucial. Information keyed in helps your consumers to learn about your location-based campaign better. Geotoko ensures you don’t miss a single detail necessary for them to take action.

Step 2: Set up your locations

For businesses with multiple store outlets, this step allows you to enter all the addresses involved in your location-based marketing campaign. Once a location is keyed in, Geotoko stores it in your address list for easy retrieval for your next campaign.

Step 3: Set up prizes

Businesses usually provide prizes to motivate customers to check-in and visit their stores. It can be quite a challenge to explain what the prizes are, how they can be won and the frequency that the prizes are given out. This page will help you get your prize details right.

Step 4: Choose your location-based services

Most of the major location-based services are available on Geotoko. Facebook Places is coming soon. Earlier, we briefly discussed the difficulty in identifying which services is best for your business. With Geotoko managing your campaign, why not pick all of them and let data surface the most appropriate location-based service for your business? Geotoko makes trial and error simple. As location-based marketing is new to many, I believe trial and error is a needed step to better learn how these location-based technologies work.

Step 5: Confirm and publish

This page summarizes all the details you have keyed in so far. Do a final check before hitting the publish button. Geotoko allows you to backtrack and edit whatever information that is entered wrongly.

Step 6: Promote your campaign

Need some advice to promote your location-based campaign? From a campaign page to spreading your message on Facebook, Geotoko has set up everything to make promotion simple. Most impressively, Geotoko automatically generates QR codes, which allows smartphone users to receive details about your campaign with a simple scan. All you have to do is to print the QR code and place it at places visible to customers. Even if no one scans it, it will at least intrigue your customers and spark off conversations.

The screen capture below shows a page with all the details about your campaign. It even has a timer to countdown the campaign expiration date, which usually spurs visitors to take action. Geotoko provides a one-stop landing page that clearly and concisely explains your campaign details to your target audience. It isn’t just about how creative your campaign is. Communicating your campaign to your target audience is equally important to trigger action.

If you wish to present a more consistent brand design, Geotoko provides custom design services for your landing page too. Below is an example of a customized Starbucks landing page:

All-in-one location-based analytics

The ease of setting up a location-based marketing campaign is just one of its core competencies. Geotoko also has a comprehensive analytical tool that tracks and analyzes the performance of your location-based campaign. The analytics dashboard shows the total check-ins and the breakdown numbers from each location-based services. It also shows the prize winners to ensure you don’t miss them.

Dig deeper, Geotoko’s heat map analysis helps you visualize where most of your check-in are coming from. Besides understanding the check-in location pattern, this data also surface the best places to execute your print ads or even guerrilla marketing gimmick. With Geotoko’s analytics, you can also track check-in and location statistics on a real-time basis too.

Analytics Dashboard:

Analytics Heatmap:

Via | Penn Olson

El día del empresario estresado | España

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Ansiedad, cefalea, dolores musculares, palpitaciones, irritabilidad, insomnio, trastornos digestivos sin causa aparente, resfriados continuos, alteraciones de la piel, caída del cabello, depresión, úlceras, cardiopatías... Si te identificas con alguno de estos síntomas seguro que…¿Eres ejecutivo?, ¿Empresario?, ¿Jefe de Sección?, ¿Coordinador de área? , ¿Delegado de Zona?, ¿MARKETER O PUBLICITARIO? ;-) ¿Si?. Venga, pues respira  hondo, ponte la chaqueta y acércate a la Plaza Pablo Ruiz Picasso este 17 de enero. Queremos que disfrutes de tu tiempo. Queremos mimarte y ayudarte a ponerle fin al estrés. Ven a conocer CBI.

Vía | Juan Sánchez - Pasión por el Marketing

 

 

 

Ten Fatal Flaws in Data Analysis

1. Where’s the Beef?

In a way, the worst flaw a data analysis can have is no analysis at all. Instead, you get data lists, sorts and queries, and maybe some simple descriptive statistics but nothing that addresses objectives, answers questions, or tells a story. If that’s all you want, that’s fine. But a data report is not a data analysis. Reports provide information; analyses provide knowledge. It’s like with your bank account. Sometimes you just want a quick report of your balance. That information has to be readily available whenever you might need it and both you and the bank have to be working with exactly the same data. If you want to assess patterns in your spending, though, you have to conduct an analysis. Say you want to figure out how much more you’re spending on commuting over the past five years, you’ll have to compile the data and scrub out anomalies, like the cross-country driving you did on vacation, to look for patterns. Analyses involve much more than a glance (http://statswithcats.wordpress.com/2010/08/22/the-five-pursuits-you-meet-in-statistics/). They take time, sometimes, a lot of time. To make sure you’re getting what you need, look beyond the data tables for models, findings, conclusions, and recommendations. If they’re not there, you didn’t get an analysis.

2. Phantom Populations

If there were to be a fatal flaw in an analysis, it would probably involve how well the samples represent the population. Sometimes data analysts don’t give enough thought to the populations they want to analyze. They use observations to make inferences to a population that doesn’t exist. Populations must be based on some identifiable commonalities that would meaningfully affect some characteristic. A group of anomalies would not be a population. Opinion polls sometimes suffer from phantom populations. Say you surveyed people wearing red shirts. Could you then generalize to everyone who wears red shirts? Canadian researchers found one such phantom population when they tried to create a control group of men who had not been exposed to pornography (http://www.telegraph.co.uk/relationships/6709646/All-men-watch-porn-scientists-find.html). Make sure the population being analyzed is more than an illusion.

3. Wow, Sham Samples

Sometimes the population is real and well defined, but the samples don’t represent it adequately. This is a common criticism of opinion polls, especially election polls. It was the reason cited for why exit polls during the presidential election of 2004 indicated that John Kerry won many precincts that ballot counts later awarded to George Bush. Medical and sociological studies may have sham samples because it is often difficult to select subjects to match some target demographic. Likewise, environmental studies can suffer from inconsistencies between soil types or aquifers. To identify sham samples, look for three things: (1) a clear definition of a real population, (2) a description of how samples were selected so that they represent the population, and (3) information about any changes that occurred during sampling, such as subjects being dropped or samples moved.

4. Enough Is Enough

The number of samples always seems to be an issue in statistical studies (http://statswithcats.wordpress.com/2010/07/17/purrfect-resolution/). For too few samples, question confidence and power; for too many samples, question meaningfulness (http://statswithcats.wordpress.com/2010/07/26/samples-and-potato-chips/). Usually analysts are ready for this question but beware if they cite the old familiar fable about using 30 samples (http://statswithcats.wordpress.com/2010/07/11/30-samples-standard-suggestion-or-superstition/). It may indicate their understanding of statistics is not as formidable as you supposed. Also, if they appear to be using a reasonable number of samples but then break out categories for further analysis, make sure each category has an appropriate number of samples for the analysis they are doing.

5. Indulging Variance

Most people don’t appreciate variance. They don’t even know it’s there (http://statswithcats.wordpress.com/2010/08/01/there%E2%80%99s-something-about-variance/). If their candidate for office is up by two percentage points in a poll, they figure the election is in the bag. Even professionals like scientists, engineers, and doctors don’t want to deal with it. They ignore it whenever they can and just address the average or most common case. Business people talk about variances all the time, only they mean differences rather than statistical dispersion. Baseball players thrive on variance. Where else can you have two failures out of every three chances and still be considered a star? Data analysts have to understand variance and address it at every step of a project. Look for how variance will be controlled in study plans (http://statswithcats.wordpress.com/2010/09/05/the-heart-and-soul-of-variance-control/
http://statswithcats.wordpress.com/2010/09/19/it%E2%80%99s-all-in-the-technique/). Look for variance to be reported with results. And most importantly, look for some assessment of how uncertainty affects any decisions made from the analysis.

6. Madness to the Methods

NASA uses checklists to ensure that every astronaut does things correctly, completely, and consistently. Make sure the analysis you are doing or reviewing takes the same care. If there are multiple data collection points or times, be sure there is a standard protocol or script for generating the data. Be especially concerned if the data are collected over multiple years. Better and cheaper methods and equipment are continuously being developed so be sure they are compatible (http://statswithcats.wordpress.com/2010/09/12/the-measure-of-a-measure/). Be sure the data had been scrubbed adequately of errors, censored and missing data, replicates, and outliers (http://statswithcats.wordpress.com/2010/10/17/the-data-scrub-3/). Finally, be sure the data analysis method is appropriate for the numbers and natures of the variables and samples (http://statswithcats.wordpress.com/2010/08/27/the-right-tool-for-the-job/).

7. Torrents of Tests

If a statistical test is conducted in a study, false positives and false negatives can be controlled, or at least, evaluated. But if there are many tests, you can bet there will be false results just because of Mother Nature’s sense of humor. In groundwater testing, for example, there may be a test for every combination of well, analytes, and sampling rounds, resulting in literally hundreds of tests. There are strategies for dealing with this type of situation, such as hierarchical testing and the use of special tests (look for the term Bonferroni). Be careful of bad decisions based on a small proportion of the tests being (apparently) significant.

8. Significant Insignificance and Insignificant Significance

Here’s where you have to use your gut feel. If a test is statistically significant and you don’t believe it should be, ask about the confidence level and whether the size of the difference is meaningful. Just as correlation doesn’t necessarily imply causation, significance doesn’t
necessarily imply meaningfulness. If something is not statistically significant and you believe it should be, ask about the power of the test and the size of the difference the test should have detected. Be sure the study looked at violations of assumptions (http://statswithcats.wordpress.com/2010/10/03/assuming-the-worst/). Also, look for what’s not there. Sometimes studies do not report nonsignificant results. Such results could be exactly what you’re looking for.

9. Extrapolation Intoxication

Make sure the data spans the parts of the variable scales about which you want to make predictions. If a study collects test data at ambient indoor temperature, beware of predictions made under freezing conditions. Likewise, be careful of tests on rabbits that are extrapolated to humans, maps showing information beyond the limits observed, surveys of one demographic extrapolated to another, and the like. Perhaps the only example of extrapolation that is even grudgingly accepted by statisticians is time-series analysis (http://statswithcats.wordpress.com/2010/08/15/time-is-on-my-side/). You have to extrapolate to predict the future. The issue is how far
into the future is reasonable, which will depend on the degree of autocorrelation, the stability of the data, and the model.

10. Misdirected Models

Models are great tools for helping you understand your data (http://statswithcats.wordpress.com/2010/08/08/the-zen-of-modeling/). Statistical models are based on data. Deterministic models, though, rely on theories, mainly the theories believed by the researcher using the model. But deterministic models are no better than the theories on which they are based. Misdirected models involve researchers creating models based on biased or mistaken theories, and then using the model to explain data or observed phenomena in a way that fits the researchers preconceived notions. This flaw is more common in areas that tend to be more observational than experimental.

Via | StatsWithCats

Developments In Sentiment Analysis

Moodagent

One of the developments we’re currently tracking is the manifestation of more tools for understanding mood and sentiment analysis. A number of services have been popping up around this idea – Littlecosm and Tweetfeel just to name a couple – of which the most notable are trying to passively gather information about mood, aggregate this sentiment in some way, and potentially provide a layer of analysis that could result in an interesting recommendation engine. We were notified by the Winamp team that they have begun to incorporate a similar type of recommendation system for their music platform now powered by Syntonetic’s Moodagent, so we took the opportunity to speak with Syntonetic CEO Peter Berg Steffensen about these ideas. Below Peter shares his insight on the thinking behind the development of Moodagent, and some brief thoughts on where sentiment analysis may be heading.

How do you see sentiment analysis + social recommendation changing over the next 3-5 years?

As the availability of music on device, in home entertainment and in-cloud nears completion, personalization will be key to entertainment fullfilment. Capturing the personal sentiment play-by-play will allow Moodagent to build engaging experiences by bridging in relevant media assets in novel ways (how about an Emotional Weather Forecast, as Tom Waits suggests?). This applies equally to music videos and other digital assets with an audio as a natural component. Expanding this to include group sentiments in social networks is a natural extension that can serve to “guide” or inspire, especially in the physical space … I´d expect the hardware and sensor manufacturers to match this with e.g. NFC heart/mood-rate detection.

What new ideas are emerging around sentiment tracking?

Together with the University of Glasgow and mobile hardware manufacturers, Moodagent is currently working on a couple of prototypes for sentiment tracking and control for both individuals and groups, but you´ll have to wait for the details until we get them out of the lab.

We currently have systems that center around things like pushing a ‘like’ button, rating systems, or tags to catalog mood. How can these things be more passive experiences?

Moodagent can map the individual history of use, and with that group use-history, we can put the experience on remote control for one and all (applying sensors where available, of course) … but we´d like to think that we can build such engaging products that our users will want to play along.

Via | psfk.com

Maybe next year...

Maybe next year...

The economy will be going gangbusters

Your knowledge will reach critical mass

Your boss will give you the go ahead (and agree to take the heat if things don't work out)

Your family situation will be stable

The competition will stop innovating

Someone else will drive the carpool, freeing up a few hours a week

There won't be any computer viruses to deal with, and

Your neighbor will return the lawnmower.

Then...

You can ship, you can launch your project, you can make the impact you've been planning on.

Of course, all of these things won't happen. Why not ship anyway?

[While others were hiding last year, new products were launched, new subscriptions were sold and new companies came into being. While they were laying low, websites got new traffic, organizations grew, and contracts were signed. While they were stuck, money was being lent, star employees were hired and trust was built.

Most of all, art got created.

That's okay, though, because it's all going to happen again in 2011. It's not too late, just later than it was.

Via | Set's Blog

Cómo optimizar la red de oficinas bancarias con geomarketing

Entre los años 2001 y 2008 las entidades financieras, sobre todo las cajas de ahorros, expandieron sus redes de oficinas hasta un ratio superior a una oficina por cada 1.000 habitantes. Todos sabemos ya cómo muchas entidades necesitan adecuar esta red a la nueva realidad  de escasez y control del crédito, muy especialmente aquellas resultantes de fusiones. Y son decisiones muy delicadas que deben ser tomadas de manera informada.

En esta entrada me propongo presentaros una metodología de racionalización de la red de oficinas. Y no es sólo un eufemismo de cierre, puesto que el método supone más bien una hoja de ruta para conocer la rentabilidad real de cada oficina, su potencial de negocio y las oportunidades de crecimiento que se le presentan, de cara a optimizar su rendimiento.

El objetivo de este análisis es recomendar, para cada oficina, una de estas cuatro estrategias:

  • Especialización en segmentos estratégicos o emergentes, como empresas, inmigrantes, seniors…
  • Diversificación del negocio en la oficina, con nuevos bienes y servicios no bancarios
  • Reducción de los recursos asignados a la oficina, con menos personal u horarios de apertura reducidos
  • O, la opción más drástica y en ocasiones la única rentable: el cierre de aquellas oficinas que no son rentables y difícilmente lo serán

La selección de oficinas no rentables

Las técnicas geoestadísticas nos permiten analizar conjuntamente el negocio real de una oficina y su negocio potencial. El negocio actual lo conocemos directamente. Asignando coordenadas geográficas a las oficinas, competencia, clientes actuales y clientes potenciales, estimamos el negocio potencial, en función de:

  • demanda en el área de influencia: residencial, transeúntes y empresas
  • presión competitiva por entidad y fecha de apertura
  • capilaridad y canibalización; el caso extremo de autocompetencia se da en la red resultante de una fusión de entidades

Comparando el negocio real con el potencial podemos discernir entre dos tipos de oficinas que no funcionan:

  • Oficinas rentabilizables. Serían aquéllas que presentan un potencial alto, bien global, bien de nicho –inmigrantes, seniors, banca privada…-. En estos casos, la decisión irá por la línea de la especialización, unida a una comunicación segmentada en el área de influencia
  • Oficinas irrecuperables. Son aquéllas en que, por falta de negocio real, potencial, alta competencia… se concluye que no es posible alcanzar el umbral de rentabilidad, por lo que se aconseja su cierre

Dado que la estimación correcta de la demanda es crítica, en Unica 360 hemos desarrollado microtarget, una serie de modelos estadísticos que predicen la demanda para diversos productos financieros. En el gráfico vemos, como ejemplo, la probabilidad de demanda de planes de pensión en el entorno de una sucursal en Paterna.


mapa de probabilidad de demanda de plan de pensión en área de influencia

El diagrama siguiente muestra cómo, para oficinas con escaso negocio real pero suficiente potencial, se deciden estrategias de supervivencia. Con aquellas oficinas de escaso potencial, se elabora  un listado inicial, sobre el que la entidad decidirá en función del potencial y otras variables.

selección de oficinas no rentabilizables, comunicación y reasignación de clientes

Plan de acción y reasignación de clientes.

A partir del análisis anterior, definimos un plan de acción para cada tipo de oficina detectado. Así, podríamos decidir una campaña de poming y street marketing enfocada a planes de pensiones en determinadas áreas en torno a ciertas oficinas con alto potencial, mientras que en oficinas con alta presencia de inmigrantes implantamos material plv específico…

Son ejemplos muy sencillos, la idea es que para cada oficina y cada microzona podemos aplicar la combinación ideal de producto, oferta y canal de comunicación, en función de las necesidades de los clientes.

En los casos de cierre o especialización drástica de la oficina, será necesario reasignar nueva oficina a los clientes actuales y potenciales, de la manera siguiente:

  • Reasignación de oficina. A cada cliente se asigna una nueva oficina, en principio la más cercana a su domicilio, aunque puede refinarse el criterio en función del histórico de operaciones.
  • Nuevas áreas de influencia recalculadas para la nueva situación, con menos sucursales en la red. Así, también los clientes potenciales son asignados a la nueva oficina de referencia.
  • Comunicación a clientes y potenciales. Se comunica por mailing o emailing a clientes actuales, personalizado con el mapa de situación de la nueva oficina. A menudo se refuerza con telemarketing para los mejores clientes. Para los potenciales lo habitual es un plan polietápico con buzoneo selectivo que genere tráfico a la sucursal, por ejemplo.

El proceso de reasignación de clientes lo realizamos de modo óptimo usando las más avanzadas técnicas geo-estadísticas, principalmente modelos de gravedad de Huff y modelos multicriterio.

Medición de resultados y optimización

Al haber usado técnicas estadísticas estandarizadas para las diferentes oficinas podemos seguir los resultados de las decisiones tomadas y comparar diferentes opciones.

  • Migración de clientes a las nuevas oficinas asignadas
  • Evolución del negocio en las oficinas receptoras de los clientes
  • Penetración en su nicho de las oficinas especializadas en segmentos
  • Análisis de cobertura de clientes y penetración por microzona tras la reordenación de la red comercial
Vía | Unica 360 - Publicado por Guillermo Córdoba